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Record: 1- Title:
- A behavioral economic reward index predicts drinking resolutions: Moderation revisited and compared with other outcomes.
- Authors:
- Tucker, Jalie A.. Department of Health Behavior, University of Alabama at Birmingham, Birmingham, AL, US, jtucker@uab.edu
Roth, David L.. Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, US
Vignolo, Mary J.. Department of Health Behavior, University of Alabama at Birmingham, Birmingham, AL, US
Westfall, Andrew O., ORCID 0000-0002-0468-4695. Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, US - Address:
- Tucker, Jalie A., Department of Health Behavior, School of Public Health, University of Alabama at Birmingham, 1665 University Boulevard, 227 Ryals, Birmingham, AL, US, 35294-0022, jtucker@uab.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 77(2), Apr, 2009. pp. 219-228.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 10
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- problem drinking, natural resolution, moderation, behavioral economics, rewards, abstinence, relapse prediction
- Abstract:
- Data were pooled from 3 studies of recently resolved community-dwelling problem drinkers to determine whether a behavioral economic index of the value of rewards available over different time horizons distinguished among moderation (n = 30), abstinent (n = 95), and unresolved (n = 77) outcomes. Moderation over 1- to 2-year prospective follow-up intervals was hypothesized to involve longer term behavior regulation processes than abstinence or relapse and to be predicted by more balanced preresolution monetary allocations between short-term and longer term objectives (i.e., drinking and saving for the future). Standardized odds ratios (ORs) based on changes in standard deviation units from a multinomial logistic regression indicated that increases on this 'Alcohol-Savings Discretionary Expenditure' index predicted higher rates of abstinence (OR = 1.93, p = .004) and relapse (OR = 2.89, p < .0001) compared with moderation outcomes. The index had incremental utility in predicting moderation in complex models that included other established predictors. The study adds to evidence supporting a behavioral economic analysis of drinking resolutions and shows that a systematic analysis of preresolution spending patterns aids in predicting moderation. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Alcohol Abuse; *Alcohol Rehabilitation; *Prediction; *Rewards; *Behavioral Economics; Relapse (Disorders); Sobriety
- Medical Subject Headings (MeSH):
- Alcohol Drinking; Choice Behavior; Economics; Female; Follow-Up Studies; Humans; Male; Middle Aged; Patient Acceptance of Health Care; Prospective Studies; Reward; Token Economy
- PsycINFO Classification:
- Drug & Alcohol Rehabilitation (3383)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Alcohol-Savings Discretionary Expenditure (ASDE) index
Drinking Problems Scale
Health and Daily Living Form-health portion
Alcohol Dependence Scale DOI: 10.1037/t00030-000
Michigan Alcoholism Screening Test DOI: 10.1037/t02357-000
Situational Confidence Questionnaire - Grant Sponsorship:
- Sponsor: National Institute on Alcohol Abuse and Alcoholism
Grant Number: R01 AA008972; K02 AA000209
Recipients: No recipient indicated - Conference:
- Annual meeting of the Research Society on Alcoholism, Annual Workshop: Mechanisms of Behavior Change in Behavioral Treatment, Third, Jul, 2007, Chicago, IL, US
- Conference Notes:
- Portions of this research were presented at the aforementioned meeting.
- Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Dec 16, 2008; Revised: Dec 5, 2008; First Submitted: Dec 27, 2007
- Release Date:
- 20090323
- Correction Date:
- 20130114
- Copyright:
- American Psychological Association. 2009
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0014968
- PMID:
- 19309182
- Accession Number:
- 2009-03774-003
- Number of Citations in Source:
- 42
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2009-03774-003&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2009-03774-003&site=ehost-live">A behavioral economic reward index predicts drinking resolutions: Moderation revisited and compared with other outcomes.</A>
- Database:
- PsycINFO
A Behavioral Economic Reward Index Predicts Drinking Resolutions: Moderation Revisited and Compared With Other Outcomes
By: Jalie A. Tucker
Department of Health Behavior, University of Alabama at Birmingham;
David L. Roth
Department of Biostatistics, University of Alabama at Birmingham
Mary J. Vignolo
Department of Health Behavior, University of Alabama at Birmingham
Andrew O. Westfall
Department of Biostatistics, University of Alabama at Birmingham
Acknowledgement: Mary J. Vignolo is now affiliated with the American College of Rheumatology Research and Education Foundation, Atlanta, GA. Andrew O. Westfall is now affiliated with the Department of Obstetrics and Gynecology, University of Alabama at Birmingham School of Medicine, and the Centre for Infectious Disease Research, Lusaka, Zambia.
This research was supported in part by Grants R01 AA008972 and K02 AA000209 from the National Institute on Alcohol Abuse and Alcoholism. The authors thank Paula D. Rippens, Bethany C. Black, and H. Russell Foushee for their contributions to the data collection phase of the research, and G. Alan Marlatt for his comments on an earlier version of this article. Portions of this research were presented at the Third Annual Workshop Mechanisms of Behavior Change in Behavioral Treatment held at the annual meeting of the Research Society on Alcoholism, Chicago, IL, July 2007.
Behavioral economic models of choice behavior have been widely applied to an analysis of substance misuse and other addictive behaviors in humans (e.g., Bickel & Marsch, 2001; Green & Kagel, 1996; Vuchinich & Tucker, 1996, 1998). Behavioral economics involves a merger of operant approaches to understanding choice behavior, particularly impulsive choice (Ainslie, 1975), with microeconomic models of consumer behavior (Rachlin, Battalio, Kagel, & Green, 1981). Both focus on how individuals allocate limited resources such as time, money, and behavior to obtain commodities available at different costs and over different delays, and strength of preference for a given commodity (e.g., drug use) is inferred from the relative resources or behavior allocated to obtain it (Premack, 1965; Rachlin, 1971). For example, the well-established matching law quantifies how humans and animals alike distribute or “match” relative response rates in proportion to the relative rates of reinforcement available from different activities (Herrnstein, 1970).
This approach is well suited to studying demand for drugs in relation to other commodities available in the natural environment (Vuchinich & Tucker, 1996, 1998). Behavioral economic models view substance misuse as a persistent preference for short-term rewards and a devaluation of larger, delayed rewards that support adaptive functioning. Research has consistently shown that preference for substance use decreases as constraints on access to the substance increase and as constraints on access to valued non-drug-related alternatives decrease (Vuchinich & Tucker, 1998). Moreover, persons with addictive behavior problems tend to devalue, or discount, delayed rewards more than normal controls (Bickel & Marsch, 2001). Control of their current behavior is less sensitive to delayed consequences, such as the adverse long-term effects of substance use.
As applied to attempts to resolve alcohol problems, these findings suggest that problem drinkers with greater sensitivity to longer term contingencies, even when drinking heavily, should have a better prognosis and that shifting control of behavior from shorter to longer term contingencies should promote resolution stability. Our earlier prospective studies of resolution attempts by community-dwelling treated and untreated problem drinkers supported this hypothesis (Tucker, Foushee, & Black, 2008; Tucker, Vuchinich, Black, & Rippens, 2006; Tucker, Vuchinich, & Rippens, 2002). Shortly after initiation of abstinence or problem-free moderation drinking, participants reported their monetary expenditures on alcoholic beverages and other commodities during the year before resolution onset using an expanded Timeline Followback (TLFB) interview (Sobell & Sobell, 1992). Establishing relative preference for alcohol through allocation of monetary resources is based on experimental work showing that the relative values of different, concurrently available commodities can be quantified by measuring choice among the commodities under varying constraints (Herrnstein, 1970; Premack, 1965). Many different activities are available in the natural environment, and monetary allocation offers a common metric to assess their relative reinforcement value (Vuchinich, Tucker, & Harllee, 1988).
Our main research focus was on studying successful natural recoveries achieved without treatment. Natural recovery samples typically include middle-income to upper income individuals (Sobell, Sobell, & Toneatto, 1992) who have complex, fixed, and recurring expenditures (e.g., mortgages, automatic payroll deductions) as well as considerable discretionary expenditures (e.g., for recreation, alcohol, voluntary savings). Because their preferences should be more readily expressed within discretionary, as opposed to fixed, spending patterns, the proportion of discretionary spending on alcoholic beverages was compared with money put into savings for future use, which was conceptualized as representing the value of rewards available over shorter and longer time horizons, respectively. Greater relative allocation to savings than to drinking, reflected in lower values on this Alcohol-Savings Discretionary Expenditure (ASDE) index, was viewed as indicating higher relative preferences for delayed rewards made possible by savings compared with more immediate rewards from drinking.
As hypothesized, problem drinkers who maintained stable resolutions had lower preresolution ASDE values than those who had unstable resolutions and relapsed at any point during the 1- to 2-year follow-ups (Tucker et al., 2002, 2006, 2008). The ASDE index had unique incremental utility in predicting stable versus unstable resolutions after controlling for established outcome predictors (e.g., problem severity, drinking practices), and it had predictive utility across intervention-naive and intervention-exposed resolution groups (Tucker et al., 2006). In addition to predicting long-term resolution stability, the ASDE index predicted drinking patterns during the early months of the postresolution period in a study that implemented Interactive Voice Response (IVR) self-monitoring with recently resolved, untreated problem drinkers (Tucker et al., 2008).
This research showed that contextually sensitive measures of the reward value of drinking in relation to other activities added unique information in an account of resolution outcomes. However, in these studies many more participants achieved stable abstinent than nonabstinent resolutions, so predictors of moderation apart from abstinence could not be investigated in the studies individually. A more extensive analysis with a larger sample that includes more participants who drank in a sustained nonproblem manner is needed to examine specific predictors of moderation. This issue has gained renewed importance as interventions continue to expand beyond abstinence-oriented treatments for alcohol-dependent persons, to include population-based public health interventions for the untreated majority with less severe problems for whom moderation is a more common and acceptable outcome (Tucker, 2003). Moderation outcomes are more common among untreated problem drinkers who quit on their own compared with the minority who seek treatment, partly because treatment seekers have more serious problems. Although early treatment research found moderation to be associated with lower problem severity, younger age, and stable life circumstances (reviewed by Miller & Munoz, 2005; Rosenberg, 2004), there have been few recent advances, with the exception that higher self-efficacy to resist drinking in high risk situations has been associated with moderation outcomes (Saladin & Santa Ana, 2004).
To obtain a sufficient sample to investigate stable moderation apart from other outcomes, we pooled the data from our three prior studies and conducted new analyses to evaluate the utility of the ASDE index in distinguishing stable nonabstinent resolutions from stable abstinent resolutions and unstable resolutions that involved problem drinking at some point over the 1- to 2-year follow-ups. Our interest in examining this issue in a re-analysis comes from early theorizing about the processes involved in moderation (Marlatt, 1985) and from preliminary findings in our IVR study (Tucker et al., 2008) that supported the theorizing. Over two decades ago, Marlatt (1985, pp. 329–344) raised the interesting but still unstudied hypothesis that abstinence and relapse are opposite ends of the same dynamic behavioral regulation process, reflecting over- and undercontrol of the daily act of drinking, respectively. Moderation was thought to involve a different regulation process that depends on “lifestyle balance” and repetitive choices to drink well within the boundaries of extreme restraint or loss-of-control drinking. To the extent that the ASDE index is a functional measure of preference for alcohol in relation to delayed rewards made possible by savings, one would expect successful moderate drinkers to organize their behavioral allocation (tracked via financial expenditures) over longer intervals compared with those who relapse or abstain. Framing Marlatt's (1985) “differential regulation” hypothesis within behavioral economic theory, lower ASDE values, reflecting more balanced monetary allocations between short-term and longer term objectives (i.e., drinking and saving for the future), should predict moderation compared with other outcomes.
The pooled data set included 30 drinkers with moderation outcomes, which was sufficient to evaluate the hypothesis that stable resolutions involving some moderation drinking over 1 to 2 years would be predicted by lower preresolution ASDE values compared with other outcomes. After this primary behavioral economic hypothesis was evaluated, established moderation predictors assessed at baseline, including problem severity, alcohol dependence, and self-efficacy, were included with the ASDE index in multinomial logistic regression models to determine if the index had unique incremental predictive utility in distinguishing outcomes among participants who resumed drinking (relapse or moderation) and among those who remained resolved (abstinence or moderation). A series of multivariable models were used to subject the ASDE index to a rigorous systematic evaluation after controlling for multiple covariates and to maintain favorable events-to-predictor ratios in each model (Peduzzi, Concato, Kemper, Holford, & Feinstein, 1996) within the limits set by the number of moderation cases in the pooled data set. We hypothesized that the ASDE index would provide significant incremental predictive utility over predictors suggested by prior research and would be particularly sensitive for distinguishing moderated and relapsed outcomes—that is, the differential regulation processes theorized by Marlatt (1985) and assessed by our behavioral economic index should be most apparent among participants who engaged in some postresolution drinking.
Method Sample Selection and Characteristics
Participants were recruited from the community via media advertisements in metropolitan areas in Alabama, Florida, Georgia, Mississippi, and Tennessee. The advertisements asked for research volunteers who had recently overcome a drinking problem with or without treatment. Respondents to the advertisements called a toll-free number, received a description of the research, and were screened with the Michigan Alcoholism Screening Test (MAST; Selzer, 1971), Alcohol Dependence Scale (ADS; Skinner & Horn, 1984), and Drinking Problems Scale (DPS; Cahalan, 1970). Eligible participants were scheduled for interviews in a place convenient for them. All studies were conducted in compliance with university institutional review board and American Psychological Association ethical standards for research with humans. Participants were informed that the research was covered by a confidentiality shield issued by the U.S. Department of Health and Human Services.
Eligibility criteria included a minimum 5-year drinking problem history (M = 16.70 years, SD = 9.31), no current other drug misuse (except nicotine), and recent cessation of problem drinking (M = 3.93 months resolved, SD = 1.78). Resolution onset was defined as the most recent date that participants began abstaining or drinking in a nonproblem manner without further heavy drinking. At all assessments, moderation was determined using criteria associated with low health risks related to drinking (Sobell et al., 1992): (a) <55 g (70 ml) of 190-proof ethanol consumed per drinking day; (b) no dependence symptoms (as assessed by the ADS); and (c) no alcohol-related negative health, psychosocial, vocational, financial, or legal consequences (as assessed by the DPS). These criteria are consistent with other drinking guidelines (e.g., National Institute on Alcohol Abuse and Alcoholism [NIAAA], 2005; World Health Organization, 2000) that generally set upper limits at ≤4 drinks/day for men and ≤3 drinks/day for women.
All of the studies included untreated problem drinkers who had initiated natural resolutions, and Tucker et al. (2006) also included a group that had received alcohol treatment from a qualified provider or attended more than two Alcoholics Anonymous meetings within about 12 months of resolution onset. Two studies (Tucker et al., 2002, 2006) required an initial resolution of 2 to 6 months and had a 2-year follow-up; the third study that involved IVR self-monitoring (Tucker et al., 2008) required a shorter initial resolution of 1 to 3 months and had a 1-year follow-up. Otherwise, the studies used identical selection criteria and follow-up procedures. Summed across studies, 205 (81.03%) of the 253 initially enrolled participants completed the 1-year follow-up required for inclusion in the pooled sample; 202 provided useable income and expenditure data and were included in the data analyses. Attrition was due to participant withdrawal or lost contact (42), significant discrepancies between participant and collateral reports of drinking (5), or death (1).
Although not an inclusion criterion, all participants met third- (Tucker et al., 2002) or fourth- (Tucker et al., 2006, 2008) edition Diagnostic and Statistical Manual of Mental Disorders criteria for alcohol dependence (American Psychiatric Association, 1987, 1994). As shown in Table 1, ADS scores fell in the moderate to low substantial dependence range (Skinner & Horn, 1984). Consistent with research showing that seeking help is associated with more severe problems, ADS, MAST, and DPS scores were relatively higher in the one study that included participants with a help-seeking history (Tucker et al., 2006). Otherwise, no differences were found across studies in preresolution drinking practices, postresolution outcomes, demographic characteristics, and preresolution income and expenditures on alcoholic beverages and money put into savings.
Sample Characteristics at Initial Assessment as a Function of Resolution Status at Follow-Up
Because our goal was to predict stable moderation apart from other outcomes, we classified participants conservatively into mutually exclusive groups based on drinking practices and problems over the entire follow-up. Those who abstained or drank moderately without problems at all follow-up points were considered to have stable resolutions, either resolved abstinent (RA) or resolved nonabstinent (RNA). Those who engaged in any problem drinking were considered to have unstable resolutions (UR), even if they later abstained or moderated.
Table 1 summarizes the sample characteristics as a function of participants' postresolution drinking status based on all follow-up data. During the initial resolution period required for inclusion, 89.1% of participants had abstained continuously and 10.9% had engaged in moderate drinking. Most participants' current drinking goal choice was informed by personal experience; 85% had made one or more serious resolution attempts in the past, with moderation attempts outnumbering abstinence attempts by about 4:1. During the present 1- to 2-year follow-up, 47.0% of participants maintained continuous or nearly continuous abstinence, 14.9% engaged in some moderate drinking with no problem drinking, and 38.1% engaged in problem drinking at some point. Of those who drank moderately, women consumed a mean of 27.5 ml of 190-proof ethanol per drinking day (SD = 8.56), and men consumed a mean of 38.9 ml (SD = 19.0), which fall within NIAAA (2005) gender-adjusted guidelines for low-risk drinking. Of those who drank heavily, over half relapsed during the first postresolution year. Mean quantities consumed per postresolution drinking day were 93.8 ml (SD = 51.1) for women and 123.8 ml (SD = 75.0) for men. During the first postresolution year, the mean and median number of drinking days for RNA participants was 73.93 (SD = 111.51) and 5.50 days, respectively, and the mean and median for UR participants was 44.23 (SD = 67.23) and 20.0 days, respectively.
Procedure
A trained interviewer conducted 1.5- to 3.0-hour individual interviews at baseline and at the annual follow-up points. After giving written informed consent, participants were administered a noninvasive breath test (Alco-Sensor III; Intoximeters, Inc., St Louis, MO) to verify sobriety. All predictors were derived from the initial interview, which covered drinking practices, life contexts, and monetary allocation during the year before participants' recent resolution up to the time of the interview. The follow-up assessments covered the time since the last interview. Brief phone interviews conducted midway between the annual follow-ups assessed drinking and help-seeking status and maintained contact. Participants received $40 for each annual interview and completion of questionnaires that they returned by mail, $10 for each phone interview, and a $50 bonus if they completed all assessments. The procedures that provided the data for the analyses are summarized in the next section and in Tucker et al. (2002, 2006, 2008).
Drinking practices and money spent on alcohol
Established TLFB procedures (Sobell & Sobell, 1992) were used to assess daily drinking practices during the preresolution year and again at each annual follow-up point. Participant reports of ounces of beer, wine, and liquor intake were converted to milliliters of 190-proof ethanol for analysis. Participants also reported how much money they spent each day on alcoholic beverages, regardless of whether the beverages were consumed. This was not excessively difficult because alcoholic beverages are sold in standard quantities, and problem drinkers typically buy and consume large quantities of a limited range of preferred beverages. As needed, TLFB interviewing techniques were used to facilitate reports of money spent on alcohol (e.g., use of anchor events, identification of sustained behavior patterns).
Monetary allocation
Participants reported their monetary income and expenditures during the same periods using an expanded set of commodity classes derived from U.S. federal consumer expenditure surveys (Vuchinich & Tucker, 1996). They were instructed to bring in financial records (e.g., bank records, paycheck stubs), and documented information was recorded first; 59.4% of participants provided some financial records. Then TLFB interviewing techniques were used to complete the financial assessment. Income in dollars was reported by source (e.g., work income, unemployment benefits, pensions, loans). Expenditures were reported in three general categories, each with subcategories, including housing (e.g., mortgage, rent, utilities), consumable goods (e.g., food, tobacco, alcohol), and other (e.g., entertainment, transportation, loan payments, money saved). Reports in each category typically involved many transactions during the assessment interval, which were summed to obtain category totals for analysis. In addition to direct verification using financial records, internal consistency and reliability checks on participants' reports of monetary allocation patterns supported their accuracy (reported in Tucker et al., 2006).
As described in Tucker et al. (2002, 2006, 2008), expenditures during the preresolution year were separated into obligatory and discretionary categories. Obligatory expenditures were for essential, ongoing, and largely fixed costs of living, including housing, food, transportation, medical, loan, and automatic payroll deductions (e.g., taxes, retirement, health insurance). Discretionary expenditures were for less essential commodities that could be purchased intermittently, including recreation, entertainment, alcohol, tobacco, other consumable goods, gifts, and elective savings. The ASDE index was computed as the proportion of discretionary expenditures summed over the preresolution year spent on alcohol minus the proportion of preresolution discretionary expenditures put into savings. ASDE values could range from 1.0 to –1.0, with lower scores representing proportionally less spending on alcohol and more on savings.
Questionnaires
After each interview, participants completed questionnaires that assessed moderation predictors in addition to those assessed during screening. Self-efficacy expectations to resist urges to drink heavily in high-risk situations were assessed using the Situational Confidence Questionnaire (SCQ; Annis & Graham, 1988), and health status was assessed using the health portion of the Health and Daily Living Form (HDL; Moos, 1985). Questionnaires were scored using established methods. Table 1 presents the total scores from the initial assessment.
Checks on data quality
In every study, in addition to checks on participants' financial reports, their reports relevant to the inclusion criteria and follow-up drinking status were assessed through collateral interviews or participant reliability checks when collaterals were unavailable. These data, summarized here, were reported previously (Tucker et al., 2002, 2006; Tucker, Foushee, Black, & Roth, 2007). Summed across studies, collaterals were interviewed at least once for 75.61% of the enrolled sample. Participant data were excluded when collaterals failed to verify participant reports relevant to the inclusion criteria or their drinking status during the follow-up. This rarely occurred (less than 2% of the initial sample of 253). For participants' retained for analysis, good to excellent agreement levels were found for drinking dimensions that could be directly observed by collaterals (e.g., alcohol-related problems, types of beverages consumed, date of initial resolution). The reliability of participant reports of drinking practices and money spent on alcohol also was examined for participants in the IVR study and was found to be excellent (Tucker et al., 2007). These findings strongly suggest that participants retained in the sample were reporting accurately.
Statistical Analysis
The mutually exclusive outcome groups were based on participants' drinking practices and problems over the entire follow-up interval: RA (n = 95)—continuous abstinence; RNA (n = 30)—some low-risk drinking with no problem drinking; or UR (n = 77)—one or more drinking episodes that exceeded the moderation criteria at any point. These conservative operational definitions deliberately separated recovering problem drinkers who resumed alcohol consumption into outcome groups on the basis of whether they engaged in any high-risk drinking, regardless of their terminal outcome status.
To evaluate the main behavioral economic hypotheses, the preresolution year monetary allocations to alcohol and savings, computed as a proportion of discretionary expenditures, were first examined as a function of drinking outcome status in a 3 (outcome status) × 2 (allocation type) analysis of variance (ANOVA) with repeated measures on the second factor. The Outcome × Allocation interaction effect from this analysis was used to determine whether the difference in these allocations, which constituted the ASDE index, was significantly related to outcome group membership. Once confirmed, we used a series of three-group multinomial logistic regression analyses to examine the utility of the ASDE index in predicting outcome group membership in relation to established predictors, including measures of problem severity (MAST, ADS, HDL physical health subscale, problem duration, help-seeking history), TLFB reports of preresolution drinking (days well functioning [abstinent and light drinking days combined], mean milliliters of ethanol consumed per drinking day), self-efficacy to resist heavy drinking (SCQ), and demographic characteristics.
Because these analyses focused on identifying predictors of moderated outcomes, the RNA group was the referent group so that the results yielded RA versus RNA and UR versus RNA contrasts and associated odds ratios (ORs) that indicated effect sizes. Although the limited RNA participants (30) prohibited comprehensive multivariable models that included all predictors simultaneously (Peduzzi et al., 1996), the RNA sample was sufficient to maintain a favorable event-to-variable ratio in a series of multinomial logistic regressions that included, first, the ASDE index alone, and then, in subsequent models, the ASDE index plus one other predictor. The latter analyses determined if the predictive utility of the ASDE index was independent of the predictive effects of each established predictor. Significant effects from the two-predictor models were then used to construct three-predictor models that evaluated whether the ASDE index continued to predict RNA outcomes beyond significant problem severity and drinking quantity measures. A final four-variable model examined whether the ASDE continued to have unique predictive utility when three other significant predictors from the three-variable models were included simultaneously. Continuous predictor variables in all logistic regression models were standardized to have a mean of 0 and a standard deviation of 1. The ORs and associated 95% confidence intervals (CIs) were based on a 1-standard deviation change in the predictors and allowed direct comparisons across predictors.
Results Tests of the Behavioral Economic Hypotheses
Table 1 summarizes univariate differences between the three outcome groups for established moderation predictors, and Table 2 summarizes group differences for the expenditure data from the preresolution year, including the ASDE index and expenditure components from which it was derived. The 3 × 2 ANOVA on the proportions of discretionary expenditures indicated an expected allocation main effect, F(1, 199) = 78.56, p < .0001, which reflected greater overall proportional allocation to drinking than savings prior to resolution. More important, as shown in Figure 1, a significant interaction effect was obtained that supported the hypotheses, F(2, 199) = 11.12, p = .0001. Comparisons using Tukey's honestly significant difference test showed that, as predicted, RNA participants had significantly lower discrepancies between alcohol and savings allocation proportions than both UR participants (p < .05) and RA participants (p < .05). RNA participants allocated proportionally less discretionary spending to alcohol and more to savings compared with UR participants (ps < .05) and less to alcohol than RA participants (p < .05).
Behavioral Economic Variables Based on Preresolution Monetary Allocation Patterns as a Function of Resolution Status at Follow-Up
Figure 1. Resolution Status × Allocation Type interaction among components of the Alcohol-Savings Discretionary Expenditure (ASDE) index based on the proportion of discretionary expenditures for alcohol and savings during the year prior to resolution onset (y axis). The error bars represent the standard errors of the drinking outcome group means.
The multinomial logistic regression analysis that included the ASDE index as the sole predictor revealed significant effects for both the UR versus RNA contrast (OR = 2.89, 95% CI = 1.77, 4.73, p < .0001) and the RA versus RNA contrast (OR = 1.93, 95% CI = 1.23, 3.02, p = .004). The OR from the first contrast indicated that a 1-standard-deviation increase in the ASDE index was associated with a 2.89-fold increase in the odds of resuming problem drinking compared with stable moderation. The OR from the second contrast indicated that a 1-standard-deviation increase in the ASDE index was associated with a 1.93-fold increase in the odds of stable abstinence compared with moderation.
The preceding logistic regression used drinking outcome assignments based on all available data from the 202 participants who had an ASDE score and at least 1 year of follow-up data. When this analysis was restricted to participants who were followed for 2 years (n = 152) and thus provided the longest continuous behavioral records, the ASDE index remained a significant predictor of both the UR versus RNA contrast (OR = 2.79, CI = 1.56, 5.00, p = .0006) and the RA versus RNA contrast (OR = 1.95, CI = 1.14, 3.32, p = .014). The same pattern of results was observed in an additional sensitivity analysis (n = 193) that excluded 4 RNA and 5 UR participants who were mostly abstinent but occasionally drank either moderately or heavily (UR vs. RNA: OR = 2.55, CI = 1.54, 4.21, p = .0003; RA vs. RNA: OR = 1.74, CI = 1.10, 2.75, p = .017). Consistent with the main behavioral economic hypothesis, frequent moderate drinkers had the lowest and frequent heavy drinkers had the highest mean ASDE scores.
Overall, these results supported the hypotheses concerning the ASDE index. The composite index separated the RNA group from the RA and UR groups, which were more similar.
Predictive Utility of the ASDE Index Relative to Established Moderation Predictors
Table 3 summarizes the results of logistic regressions that included the ASDE index and one other moderation predictor and also presents the correlations between the ASDE index and the other predictors. Four findings are noteworthy. First, the ASDE index showed low to modest correlations with all other predictors, ranging from .00 to .36 (rs > .14 or < −.14 were significant at p < .05), indicating that the ASDE was largely unrelated to the other predictors and capable of contributing new information to the prediction of outcomes in the multivariable models. Second, the analyses replicated many established moderation predictors, including lower dependence, fewer psychosocial and health problems, shorter problem durations, lower quantities consumed on drinking days, absence of help seeking, stable sociodemographic characteristics, and higher self-efficacy. Third, when the two-predictor models were run, the ASDE index remained a robust predictor of outcomes among the subset of participants who drank during the follow-up. The UR versus RNA contrast was significant in all models that included another predictor, indicating that the ASDE index explained unique variance after accounting for the other predictor. Fourth, the ASDE index distinguished outcomes among the subset of participants who maintained resolution. The RA versus RNA contrast was significant for the index when it was included with another predictor in all but two models that included either the MAST or mean milliliters per drinking day.
Multinomial Logistic Regressions Using the ASDE Index and One Established Moderation Predictor
On the basis of these results, additional three-variable logistic regressions were conducted that included the ASDE index and two other variables of conceptual interest or empirical utility. The MAST and ADS were not included in the same model because they were highly correlated (r = .68, p < .001) and would, therefore, be largely redundant in the same model. Table 4 presents the results of the three-variable models that we examined. In all eight complex models, the ASDE index continued to separate the UR and RNA groups. The index was not highly effective in separating the RA and RNA groups, although trends that approached significance for the ASDE were observed in Models 1, 3, and 8. A positive help-seeking experience and mean milliliters ethanol/drinking day consistently separated all three outcome groups, with absence of help seeking and lower quantities consumed being associated with an RNA status. The MAST, ADS, and SCQ contributed significantly to the separation of the UR and RNA groups. The MAST and ADS also separated the RA and RNA groups in models that did not include drinks per drinking day, but inclusion of that variable attenuated their predictive utility for the RA–RNA contrast.
Multinomial Logistic Regression Models Using the ASDE Index With Two Moderation Predictors
A final, comprehensive four-variable model was constructed from the strongest predictors identified in Table 3, namely the MAST, SCQ, mean milliliters ethanol/drinking day, and ASDE index. For the UR–RNA contrast, the SCQ (OR = 0.30, CI = 0.12, 0.74, p = .009), mean milliliters ethanol/drinking day (OR = 7.26, CI = 1.77, 29.74, p = .006), and ASDE (OR = 2.46, CI = 1.27, 4.77, p = .008) were statistically significant unique predictors, whereas for the RA–RNA contrast, the MAST (OR = 1.96, CI = 1.03, 3.72, p = .04) and mean milliliters ethanol/drinking day (OR = 9.54, CI = 2.37, 38.33, p = .002) were the statistically significant unique predictors.
DiscussionThe findings add to evidence supporting a behavioral economic analysis of drinking resolutions and extend the utility of a measure of preference for alcohol derived from preresolution spending patterns to predict moderation. Stable resolutions involving moderate alcohol use over 1- to 2-year follow-ups were associated with proportionally more preresolution discretionary monetary allocation to savings and less to alcohol compared with continuously abstinent resolutions and unstable resolutions that involved some problem drinking. Lower ASDE values presumably reflect more balanced monetary allocations between short-term and longer term objectives, suggesting that the temporal intervals over which problem drinkers organize and allocate their behavior, even while drinking heavily, may help identify those most able to make a transition to stable moderate use.
The support for the ASDE index in this re-analysis of pooled data from prior studies was obtained in conjunction with results that replicated established moderation predictors. As found during the controlled drinking debate (cf. Marlatt, 1983), moderation was associated with greater social stability and with lower problem severity, including shorter drinking histories, lower alcohol dependence and quantities consumed per drinking day, and fewer alcohol-related psychosocial problems (Miller & Munoz, 2005; Rosenberg, 2004). Higher self-efficacy to resist heavy drinking in high-risk situations also predicted moderation, which replicated recent findings that added this variable to those identified during the controlled drinking debate (Saladin & Santa Ana, 2004).
When included in models with other significant predictors, the ASDE index added unique information to the prediction of moderation and was especially effective in distinguishing outcomes among participants who engaged in postresolution drinking. The ASDE index was significant for the RNA–UR contrast in all models evaluated. Drinkers who maintained moderation had lower ASDE, MAST, and ADS scores and higher self-efficacy scores than those who relapsed.
The ASDE index also predicted outcomes among participants who remained resolved, separating the abstinent and moderation groups in models that included the ASDE alone or with one other predictor. The index was less effective in separating these groups in complex models that included quantities consumed, an attenuation that may be due in part to heterogeneity in the RA group. Some abstainers may be able to drink moderately but have not exposed themselves to postresolution alcohol use, whereas others might resume problem drinking. This unobserved moderation versus relapsed outcome among abstainers can, therefore, limit the full predictive significance of individual predictors of moderation outcomes. Given this attenuated separation, for purposes of choosing an initial abstinence or moderation drinking goal, it seems prudent clinically to require multiple favorable indicators of likely success at moderation until further research can establish decision-making algorithms that satisfactorily separate all three outcome groups. The present findings suggest that supplementing the MAST, ADS, and SCQ with questions about consumption quantities on drinking days and money spent on alcohol and put into savings provides a sound basis for making clinical judgments about initial drinking goal choice.
The ASDE findings also have implications for behavioral economic research and addictive behavior change applications. Evidence is accumulating that addictive behaviors are characterized by a foreshortened view of the future and that successful behavior change will likely involve a shift from a shorter to a longer view of the future and organizing behavior accordingly. In addition to the present support based on money allocation patterns in the natural environment, behavioral economic research on temporal discounting of hypothetical money and health outcomes has consistently found steeper discount functions in smokers, problem drinkers, opiate addicts, and gamblers (Bickel & Marsch, 2001). Social psychological studies have similarly found that the time perspectives of substance abusers are more present oriented and less future oriented than for normal controls (Henson, Carey, Carey, & Maisto, 2006; Keough, Zimbardo, & Boyd, 1999). Addiction treatment outcomes also are predicted by behavioral impulsivity measures that span laboratory and naturalistic assessments, including delay discounting of hypothetical rewards (e.g., Yoon et al., 2007), demand curve analysis based on hypothetical alcohol purchase tasks (MacKillop & Murphy, 2007), questionnaire measures of relative reinforcement value (e.g., Murphy, Correia, Colby, & Vuchinich, 2005; Schmitz, Sayre, Hokanson, & Spiga, 2003), and experiential discounting tasks that assess delay discounting using real, rather than hypothetical, monetary rewards (Krishnan-Sarin et al., 2007).
Such measures of behavioral impulsivity developed within a behavioral economic framework guided by the matching law (Herrnstein, 1970) show inconsistent relationships with personality questionnaires that assess traitlike impulsive tendencies, and the latter measures have no utility in predicting treatment outcomes (e.g., Krishnan-Sarin et al., 2007). Although it remains to be determined the extent to which the behavioral economic measures assess common or different dimensions of behavioral impulsivity and the relative reinforcing efficacy of substances (MacKillop & Murphy, 2007; Reynolds, Richards, Horn, & Karraker, 2004), they appear to measure functional changes in preferences for substance use and nondrug alternatives that are at the core of the dynamic addictive process. The ASDE index as currently assessed is decidedly among the more molar behavioral economic measures and appears to measure the relative reinforcement value of alcohol in the context of resource allocation to commodities available over different temporal intervals. Although the ASDE may prove to be less sensitive to short-term preference shifts than are brief measures of the current demand for drugs, it provides a comprehensive benchmark based on behavior patterns in the natural environment against which to evaluate the utility of briefer measures of relative preferences, including laboratory preparations.
Regardless of whether foreshortened views of the future are a cause or consequence of the addictive process, or both, it seems likely that interventions may facilitate positive change by promoting contact with the set of delayed positive consequences, or the sober “consumption bundle,” that typically flows from a sober lifestyle. Behavior patterns with delayed positive consequences often are more valuable as a whole compared with discrete acts chosen day-to-day (Rachlin, 1995), and the likelihood of maintaining longer term, higher yield patterns is presumably greater once contact is made with the delayed positive consequences. Drawing attention to delayed consequences or signaling their future availability (e.g., via self-monitoring, motivational interviewing, or decisional balance exercises) is one way to shift behavioral organization toward the future. Such approaches may reduce the appeal of short-term discrete rewards, like substance use, by helping people frame choices as involving an extended series of linked behaviors, events, and outcomes with higher overall value (Chapman, 1996; Rachlin, 1995).
The present research has limitations that merit attention in future studies. First, despite pooling across studies, the number of participants with stable moderation resolutions was still relatively small, which limited the number of predictors that could be evaluated simultaneously in multivariate models. Although quite coherent across models, the results merit replication with a larger sample of moderate drinkers. Second, because two of the earlier studies (Tucker et al., 2002, 2006) included too few moderate drinkers to analyze them separately from abstainers, the positive ASDE findings in those studies may have been amplified by including RNA participants in the stable resolution group along with RA participants. However, RNA participants constituted less than 12% of the stable resolution groups in these studies, suggesting that the overall ASDE results and interpretation were appropriate. Third, future studies of resolution should expand the window of selection to include problem drinkers who have resolved quite recently or are contemplating doing so. Participants with drinking problems that fall short of clinical diagnostic criteria also should be studied, and drinking-related inclusion criteria should be relaxed to allow research on outcomes that fall short of stable moderation but entail substantial reductions in drinking and related harm.
Pursuing the public health implications of understanding pathways to and predictors of moderation will require expanding the scope of research to include new concepts and methods, such as those provided by behavioral economics. Better characterizing the moderate use of alcohol by individuals with a history of problem drinking may provide new insights about the behavior regulation processes involved in resolution and relapse and help guide innovative behavior change strategies that increase contact with the population with problems.
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Submitted: December 27, 2007 Revised: December 5, 2008 Accepted: December 16, 2008
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Source: Journal of Consulting and Clinical Psychology. Vol. 77. (2), Apr, 2009 pp. 219-228)
Accession Number: 2009-03774-003
Digital Object Identifier: 10.1037/a0014968
Record: 2- Title:
- A communication-based intervention for nonverbal children with autism: What changes? Who benefits?
- Authors:
- Gordon, Kate. Institute of Psychiatry, King’s College London, London, England, kate.gordon@kcl.ac.uk
Pasco, Greg. Centre for Research in Autism and Education, Department of Psychology and Human Development, Institute of Education, London, England
McElduff, Fiona. Department of Paediatric Epidemiology and Statistics, Institute of Child Health, London, England
Wade, Angie. Department of Paediatric Epidemiology and Statistics, Institute of Child Health, London, England
Howlin, Pat. Institute of Psychiatry, King’s College London, London, England
Charman, Tony. Centre for Research in Autism and Education, Department of Psychology and Human Development, Institute of Education, London, England - Address:
- Gordon, Kate, Institute of Psychiatry, King’s College London, Addiction Sciences Building, 4 Windsor Walk, SE5 8AF, London, England, kate.gordon@kcl.ac.uk
- Source:
- Journal of Consulting and Clinical Psychology, Vol 79(4), Aug, 2011. pp. 447-457.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- autism, communicative form, communicative function, intervention response predictors, psychosocial intervention, communication-based intervention
- Abstract:
- Objective: This article examines the form and function of spontaneous communication and outcome predictors in nonverbal children with autism following classroom-based intervention (Picture Exchange Communication System [PECS] training). Method: 84 children from 15 schools participated in a randomized controlled trial (RCT) of PECS (P. Howlin, R. K. Gordon, G. Pasco, A. Wade, & T. Charman, 2007). They were aged 4–10 years (73 boys). Primary outcome measure was naturalistic observation of communication in the classroom. Multilevel Poisson regression was used to test for intervention effects and outcome predictors. Results: Spontaneous communication using picture cards, speech, or both increased significantly following training (rate ratio [RR] =1.90, 95% CI [1.46, 2.48], p < .001; RR = 1.77, 95% CI [1.35, 2.32], p < .001; RR = 3.74, 95% CI [2.19, 6.37], p < .001, respectively). Spontaneous communication to request objects significantly increased (RR = 2.17, 95% CI [1.75, 2.68], p < .001), but spontaneous requesting for social purposes did not (RR = 1.34, 95% CI [0.83, 2.18], p = .237). Only the effect on spontaneous speech persisted by follow-up (9 months later). Less severe baseline autism symptomatology (lower Autism Diagnosis Observation Schedule [ADOS] score; C. Lord et al., 2000) was associated with greater increase in spontaneous speech (RR = 0.90, 95% CI [0.83, 0.98], p = .011) and less severe baseline expressive language impairment (lower ADOS item A1 score), with larger increases in spontaneous use of speech and pictures together (RR = 0.62, 95% CI [0.44, 0.88], p = .008). Conclusion: Overall, PECS appeared to enhance children's spontaneous communication for instrumental requesting using pictures, speech, or a combination of both. Some effects of training were moderated by baseline factors. For example, PECS appears to have increased spontaneous speech in children who could talk a little at baseline. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Autism Spectrum Disorders; *Communication; *Intervention; Communication Systems
- Medical Subject Headings (MeSH):
- Autistic Disorder; Child; Child, Preschool; Communication Disorders; Female; Follow-Up Studies; Humans; Male; Nonverbal Communication; Treatment Outcome
- PsycINFO Classification:
- Developmental Disorders & Autism (3250)
Health & Mental Health Treatment & Prevention (3300) - Population:
- Human
Male
Female - Location:
- England
- Age Group:
- Childhood (birth-12 yrs)
Preschool Age (2-5 yrs)
School Age (6-12 yrs) - Tests & Measures:
- Autism Diagnosis Observation Schedule
Expressive One Word Picture Vocabulary Test
Mullen Scales of Early Learning - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Apr 20, 2011; Revised: Dec 28, 2010; First Submitted: Mar 25, 2010
- Release Date:
- 20110725
- Correction Date:
- 20151207
- Copyright:
- American Psychological Association. 2011
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0024379
- PMID:
- 21787048
- Accession Number:
- 2011-15510-002
- Number of Citations in Source:
- 45
- Persistent link to this record (Permalink):
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2011-15510-002&site=ehost-live">A communication-based intervention for nonverbal children with autism: What changes? Who benefits?</A>
- Database:
- PsycINFO
A Communication-Based Intervention for Nonverbal Children With Autism: What Changes? Who Benefits?
By: Kate Gordon
Institute of Psychiatry, King's College London, England;
Greg Pasco
Centre for Research in Autism and Education, Department of Psychology and Human Development, Institute of Education, London, England
Fiona McElduff
Department of Paediatric Epidemiology and Statistics, Institute of Child Health, London, England
Angie Wade
Department of Paediatric Epidemiology and Statistics, Institute of Child Health, London, England
Pat Howlin
Institute of Psychiatry, King's College London, England
Tony Charman
Centre for Research in Autism and Education, Department of Psychology and Human Development, Institute of Education, London, England
Acknowledgement: We thank all the participating schools, children, and their parents; PECS Consultants Sue Baker and Teresa Webb from Pyramid UK; and The Three Guineas Trust for their generous support of this project.
Over the past two decades, there has been accumulating evidence for the effectiveness of psychosocial programs for young children with autism. These tend to incorporate a mix of behavioral, developmental, and educational approaches, and although methods may vary, their general goals are to enhance cognitive, communication, and social skills while minimizing rigid and repetitive and other problem behaviors (see Lord & McGee, 2001; Rogers & Vismara, 2008, for reviews). In part due to the design of studies, however, there have been few opportunities to examine the detail of exactly what changes and who benefits as a result of these interventions.
In terms of measuring what changes, many early autism intervention studies, in particular within the applied behavior analysis field, used global measures of outcome such as IQ scores and school placement (Dawson & Osterling, 1997; Howlin, Magiati, & Charman, 2009). More recent studies have used a wider range of measures, including standardized tests of adaptive behavior, expressive and receptive language, and measures of autism severity (Howard, Sparkman, Cohen, Green, & Stanislaw, 2005; Remington et al., 2007). Some studies have attempted to include measures of change in skills or behaviors specifically targeted by the intervention, for example, parents' or carers' knowledge about autism (Jocelyn, Casiro, Beattie, Bow, & Kneisz, 1998). Furthermore, a small number of studies include naturalistic or quasinaturalistic measures of communication, for example, observing spontaneous communication and language in parent–child interactions (Aldred, Green, & Adams, 2004); observing parents' use of facilitative strategies during social interaction with their child (McConachie, Randle, Hammal, & Le Couteur, 2005); recording the frequency and rate of children's turn-taking, joint attention, and requesting behaviors (Yoder & Stone, 2006a, 2006b); and observing structured play and joint attention acts within parent– and experimenter–child interactions (Kasari, Freeman, & Paparella, 2006; Kasari, Paparella, Freeman, & Jahromi, 2008). In addition to increasing the face validity of the research, demonstration of change in specific behaviors is likely to be helpful in elucidating exactly how an intervention is working (Kazdin & Nock, 2003).
To examine who benefits from intervention, attempts have been made to study the effects of preintervention child characteristics on outcome, most notably IQ and age. For IQ, this usually involves examining correlations between preintervention IQ and postintervention outcome (Eldevik, Eikeseth, Jahr, & Smith, 2006; Harris & Handleman, 2000; Remington et al., 2007) or the comparison of outcomes for high-IQ versus low-IQ subgroups (Aldred et al., 2004; Ben Itzchak, Lahat, Burgin, & Zachor, 2008; Ben-Itzchak & Zachor, 2007). Significant positive associations have been found between preintervention IQ and outcome. However, Yoder and Compton (2004) highlighted the flaws of testing for moderators by exploring correlations or comparison of subgroups' effect sizes. They emphasized the importance of using statistical methods that enable differentiation of predictors of growth or progress from predictors of intervention response. Where participants have been randomly assigned to intervention or control conditions, the appropriate method for identifying predictors of intervention response is to test for statistical interactions between child characteristics and group assignment in relation to the outcome variables. Although such statistical tests of moderator effects are well established in medical trial literature and used increasingly in the psychiatry field (Kraemer, Wilson, Fairburn, & Agras, 2002) to date, they have only been used in two studies of autism intervention (Kasari et al., 2006, 2008; Yoder & Stone, 2006a, 2006b).
Kasari et al. (2006) randomized 58 preschool children with autism (aged 3–4) to either joint attention training, symbolic play training, or a control condition, demonstrating that both active interventions were effective at enhancing social communication skills. In a later study, they presented further analysis revealing that growth in expressive language was positively predicted by a number of joint attention and symbolic play variables (Kasari et al., 2008). Yoder and Stone (2006a, 2006b) used multiple regression to demonstrate the moderating effects of baseline joint attention abilities. Their randomized trial (N = 36) compared Picture Exchange Communication Training (PECS; Bondy & Frost, 1998) with Responsive Education and Prelinguistic Milieu Training (RPMT; Yoder & Warren, 1999). Children who initiated joint attention relatively more frequently at baseline benefited more from RPMT in terms of their postintervention frequency of joint attention initiations, whereas children who initiated joint attention less frequently at baseline benefited more from PECS (Yoder & Stone, 2006a). In a later analysis, using mixed-level modeling, they found object exploration also moderated intervention response (Yoder & Stone, 2006b). Thus, children who displayed object exploration behaviors more frequently at baseline benefited most from PECS, showing greater increases in production of nonimitative words at outcome. The children who showed lower object exploration at baseline benefited more with respect to word production if they had received RPMT.
These studies notwithstanding, to date relatively few studies of psychosocial intervention for children with autism have effectively investigated what changes in response to treatment and who benefits. Common use of standardized IQ and language assessments to measure outcome has meant there has been relatively little naturalistic analysis of change in the form and function of children's communication as a result of intervention. Insufficiently sized samples and lack of randomization have impeded investigation into treatment moderators.
The Present StudyEnhancing spontaneity in everyday communication has been highlighted as one of the most important goals of intervention for children with autism (Lord & McGee, 2001). The aim of PECS is to teach spontaneous and functional communication to children with autism in a social context (Bondy & Frost, 1998). PECS is widely used in home and educational settings (Preston & Carter, 2009; Sulzer-Azaroff, Hoffman, Horton, Bondy, & Frost, 2009), and there is evidence from a number of single-case and case-series studies (Charlop-Christy, Carpenter, Le, LeBlanc, & Kellet, 2002; Ganz & Simpson, 2004; Kravits, Kamps, Kemmerer, & Potucek, 2002; Magiati & Howlin, 2003; Schwartz, Garfinkle, & Bauer, 1998), a school-based controlled study (Carr & Felce, 2007), and two randomized controlled trials (RCTs) (Howlin, Gordon, Pasco, Wade, & Charman, 2007; Yoder & Stone, 2006a, 2006b) that PECS can lead to improved communication skills in this group.
Howlin et al. (2007) conducted a pragmatic RCT of PECS. The study was designed to test the “real world” effectiveness of PECS. Initial training was delivered to teachers and classroom assistants by PECS consultants at workshops. PECS was subsequently applied by school staff in the classroom under regular supervision by PECS consultants. Naturalistic observations of rates of children's communication were used as the primary outcomes to measure intervention effects. Immediately after training had ended (approximately 5 months), the rate at which children spontaneously initiated communication (IC) had significantly increased. Overall rates of children's use of picture cards (P) to communicate (i.e., spontaneous or prompted) had also significantly increased. By the 9-month follow-up, however, these effects had disappeared. Overall rates of children's speech/vocalization (S) (including spontaneous and prompted) did not increase.
In the present article, using the sample from Howlin et al. (2007), we explore exactly which communication forms were used by children more spontaneously as a result of PECS training, which communicative functions increased and which children benefited most from the intervention. We aimed to build on previous studies by applying appropriate analysis to this relatively large sample to address the following questions:
1. Did PECS training act specifically to increase children's spontaneous communication using the picture cards or did its effect generalize to support greater spontaneity using speech as well? Previous studies have demonstrated that PECS supports children to communicate spontaneously using picture cards (e.g., Bondy & Frost, 1994; Charlop-Christy et al., 2002; Ganz & Simpson, 2004; Kravits et al., 2002) and sometimes speech (e.g., Bondy & Frost, 1994; Charlop-Christy et al., 2002; Ganz & Simpson, 2004). Two studies using more naturalistic measures of outcome also suggest that PECS can increase spontaneous communication using picture cards and speech together (Carr & Felce, 2007; Yoder & Stone, 2006a).
2. Did PECS increase children's spontaneous communication purely for instrumental requesting or did the training also lead to increased spontaneous communication for more social purposes? PECS training initially focuses on teaching children to make requests for objects. Later training phases aim to broaden the range of communicative functions, such as sharing attention through commenting (Bondy, Tincani, & Frost, 2004). Studies have shown that PECS training can be used successfully to teach children spontaneous requesting for objects (Carr & Felce, 2007; Ganz & Simpson, 2004; Kravits et al., 2002), and some have demonstrated effects on other forms of noninstrumental, more social communication (Schwartz et al., 1998; Yoder & Stone, 2006b).
3. Which children benefited most from PECS training? PECS was specifically developed for children with autism to obviate the need for prerequisite communication skills. It might be hypothesized therefore that response to PECS training would not be predicted by such factors as language comprehension skills or autistic symptomatology. Within the autism intervention literature more generally, however, higher IQ has been associated with better outcome (e.g., Harris & Handleman, 2000; Schwartz et al., 1998). Yoder and Stone's (2006a, 2006b) studies have been the only systematic investigation of PECS response predictors to date, finding that children who were most impaired in terms of baseline language and joint attention skills were those who gained most from PECS training. Given the lack of research in this area, we took an exploratory approach in the present study, investigating the potential moderating effect on PECS training of four factors measured at baseline: chronological age, expressive language, autistic symptomatology, and cognitive ability.
Method
Participants of the RCT
Eighty-four children (73 boys, 11 girls) from 17 classes in special needs elementary schools participated in the study. Classes were broadly similar, with a child–adult ratio of approximately 2:1. Class teaching programs varied, but most classes adopted an eclectic approach incorporating a range of visual systems and structured teaching, often based on the TEACCH methodology (Mesibov, Shea, & Schopler, 2004). Picture cards were present in most classrooms in the treatment and nontreatment groups even at baseline, though these were not necessarily used according to PECS principles. All schools were situated in Greater London or the south east of England. Children were aged between 4 and 10 years (mean age at baseline = 6.8 years, SD = 1.26), and all had an intellectual disability. Ethnicity, socioeconomic status, and comorbidity data were not formally collected. This was a community-based study that included all suitable children whose parents consented. To be eligible, children had to (a) have a formal clinical diagnosis of autism, (b) use little or no functional language (i.e., no more than single words), (c) have no sensory impairment, and (d) be aged between 4 and 11 years and (e) not using PECS beyond Phase 1.
Informed consent and ethical approval
Written informed consent to participate in the study was obtained from the parent or guardian of each child and from a senior member of staff from each school. The original trial protocol was prospectively reviewed and approved by the Wandsworth Local Research Ethics Committee (Ref. IAS/der/02.42.6).
Describing the group at baseline
To obtain baseline data on autism severity, all children were assessed using Module 1 of the Autism Diagnosis Observation Schedule (ADOS; Lord et al., 2000). The ADOS is an interactive semistructured assessment of communication, social interaction, imagination, and repetitive and stereotyped interests. Assessment consists of a range of activities and social presses providing a standardized context in which to observe specific behaviors. There are four modules. Module 1 was used in this study, designed for use with preverbal individuals or for those whose expressive language is still at single word or simple phrase level. Seventy-five children met the ADOS criteria for a diagnosis of autism; nine children met criteria for autism spectrum disorder. Score from Item A1 of the ADOS was used as an index of expressive language ability (0 = regular use of utterance of two or more words; 1 = occasional phrases only, mostly single words; 2 = recognizable single words only; 3 = at least one word or word approximation but fewer than five words; 4 = no words or word approximations). Thirty-eight children (45%) used no words or word approximations during the ADOS, 31 (37%) used single words, and 15 (18%) used at least one phrase. Most children (64%) scored 0 on the Expressive One Word Picture Vocabulary Test (Brownell, 2000). Nonverbal developmental quotient (NVDQ = nonverbal mental age equivalent/chronological age ×100) was ascertained using the Mullen Scales of Early Learning (Mullen, 1995). Group median NVDQ was 29.90 (interquartile range was 21.20–40.52). In summary, the sample comprised children with clear autism and who were very impaired with regard to verbal and nonverbal skills.
Design of the RCT
As a group-randomized control trial, class groups (each including approximately six children and two to three staff) were assigned into one of three intervention groups. The immediate treatment group (ITG; five class groups, 26 children) received training immediately after the baseline assessment; the delayed treatment group (DTG; six class groups, 30 children) received training about 9 months later, immediately after Time 2 assessment; the no-treatment group (NTG; six class groups, 28 children) received no training. Staggering of treatment across two time periods maximized the number of children involved in the study and allowed investigation of the continued effectiveness of any immediate treatment effects noted. The data analyses incorporated each child contributing all measurements within all control, treatment, and posttreatment periods; thus, statistical power was not compromised by the three-arm approach to data collection. Differences between the three groups at baseline were analyzed and reported in Howlin et al. (2007). The analysis was designed to adjust for these differences. Figure 1 shows recruitment, the points at which intervention was delivered, and when each of the three groups was observed.
Figure 1. Flow chart illustrating sample selection, recruitment, training, and outcome assessment. Adapted from “The Effectiveness of Picture Exchange Communication System (PECS) Training for Teachers of Children With Autism: A Pragmatic, Group Randomised Controlled Trial,” by P. Howlin, R. Kate Gordon, G. Pasco, A. Wade, and T Charman, 2007, Journal of Child Psychology and Psychiatry, 48, pp. 473–481. Copyright 2007 by John Wiley and Sons.
Outcome measurement
The outcome measure was a 15-min videotaped observation, intended to be an ecologically valid measure of communication skills. Children were filmed in their class snack sessions at Time 1 (baseline) and twice further over a period of 20 months (2 academic years). Snack sessions were selected as these were likely to create the most opportunities for children to make spontaneous requests. Furthermore, daily snack sessions occurred in all the classes and were broadly similar. These sessions usually lasted approximately 15 min and involved all children and class staff sitting at tables in the classroom or school kitchen. Drinks and food snacks such as fruit or cookies were given out or were on offer for children to request. Where classes used picture cards, these were usually made available for children (e.g., by placing a large board at the front of the classroom or by handing out books with the cards inside).
Children's communication was coded from the videotape using an observation schedule designed specifically for this study (Classroom Observation Schedule for Measuring Intentional Communication; Pasco, Gordon, Howlin, & Charman, 2008). The primary outcome variable was frequency of child-initiated communication (IC). Frequencies of different communication modalities used (such as the number of times a child used a picture card [P] and/or speech/vocalization [S] to communicate) were also recorded; communication functions were recorded by counting each time a child communicated for the purpose of requesting objects (R) and for the purpose of requesting a social interaction or commenting (D). In this way, a single communication act might produce three or more codes, for example, as a spontaneous initiation (IC), of the use of a picture card (P), and for the purpose of requesting (R).
Data analysis
Where outcomes are numerical counts of relatively rare events, Poisson regression is a useful method for analysis (Dobson, 2002). The Poisson regression model expresses the log outcome rate as a linear function of a set of predictors. In this study, Poisson regression models were produced for each of the five outcome variables of interest, concerned with form or function of children's spontaneously initiated communication: spontaneous communicative initiation using picture cards (IC-P); spontaneous communication using speech (IC-S); spontaneous communication using both simultaneously (IC-PS); spontaneous communication to request for objects (IC-R), and spontaneous communication to request for social routine or commenting (IC-D). The regression models were created within the Stata IC Version 10 (StataCorp., 2003).
As can be seen in Figure 1, the data set comprised data from three time points in the three different experimental groups. Multilevel models were used that took account of the longitudinal nature of the measurements, time trends, differing treatment regimes within the same individuals over time, and within child correlations between repeated measurements (Goldstein, 2003). The standard errors of the model parameters were thus adjusted for any within-child (across time) or within-class (between children) correlations. Models also allowed adjustment for any group differences at baseline in terms of age, developmental level, expressive language, and autistic symptom severity. Each model included an independent binary intervention variable (i.e., intervention or no intervention), a further binary variable to denote follow-up (this occurred at Time 3 for the ITG group only), a time variable (continuous in order to adjust for differences in the actual lengths of time between observations, i.e., Time 1 = 0 days), and an offset to adjust for the difference in the lengths of snack times for individual children.
In Poisson regression, effect size is represented by the rate ratio (RR) that estimates the relative rate of change in the mean number of events attributable to each explanatory variable. For example, for a binary intervention variable, the RR represents the relative difference in mean frequency of spontaneous initiations for children in the intervention group compared with those not in the intervention group. For continuous variables (e.g., baseline age [months]), the RR represents the relative difference in the mean frequency of initiations for every increase in one unit of the explanatory variable—in this case, for every month older the child was at baseline. An RR of 1 indicates no change, and so, for example, an RR of 1.2 represents an increase of 20% for each unit increase; an RR of 0.7 represents a decrease of 30% for each unit increase. RRs for estimates from the five models (for each of the five outcome variables) are reported along with 95% confidence intervals and p values. These models were not independent and were interpreted jointly taking into account the relationship between the various outcomes.
Testing for intervention moderators
Where PECS had a significant effect, a second round of analyses was conducted in order to identify potential intervention response moderators. If baseline factors (i.e., chronological age, autistic symptomatology, expressive language, or developmental quotient) independently predicted progress at postintervention (shown in Table 2), tests for Intervention × Baseline Factor interactions were conducted to explore whether they also predicted a specific intervention response. The RR for the interaction term represents the impact of the baseline variable on the outcome over and above any existing variance due to a main intervention effect or a main effect of the baseline factor.
Rate Ratio Estimates (and 95% Confidence Intervals) for Each of the Five Outcome Variables
Results
The results are presented in two parts. First, we examined the impact of PECS training on children's spontaneous communication using three different communication modalities and for two different functions. Second, controlling for differences, we tested baseline variables for their potential moderating effect on the intervention.
Change in spontaneous communication following PECS training
Table 1 shows the median rate of initiations per minute for the five variables in each of the three treatment arms at each of the three time points. The rates in bold are immediately following PECS training in the ITG and DTG. The italicized rates are at 9-month follow-up (ITG only). These figures indicate some changes following PECS training. For example, in the DTG, the median rate of spontaneous initiation of communication using picture cards went from 0 to 0.44 per minute, that is, more than 6 times per 15-min snack session. In the ITG, the median rate of spontaneous communication using speech or vocalization went from 0.03 to 0.13 per minute, and in the DTG, the median rate of spontaneous requesting rose from 0.03 to 0.46 times per minute. Despite these group effects, for each of the form and function variables, some children remained at zero, even after PECS training. For example, of the 56 children in the ITG and DTG, 12 were still not using picture cards to spontaneously communicate at all after the training, and nine were still not making spontaneous requests.
Median Initiations Made Per Minute at Each Time Point for Each of the Three Treatment Groups
Table 2 shows the results of the Poisson analysis for each variable. RRs are shown for change attributable to the intervention immediately post-PECS training, at 9-month follow-up (for the ITG group only), and for each of the nonintervention variables measured at baseline. Initiations using picture cards (IC-P), using speech (IC-S), and using both simultaneously (IC-PS) all increased significantly following training (RR = 1.90, 95% CI [1.46, 2.48], p < .001; RR = 1.77, 95% CI [1.35, 2.32], p < .001; RR = 3.74, 95% CI [2.19, 6.37], p < .001, respectively). The average increase observed was similar in size for IC-P and IC-S and about twice as large for IC-PS. However, it should be noted that the confidence intervals are wide, and in all instances the data are compatible with a twofold increase in the RR, so we cannot necessarily infer that the effect is any greater for IC-PS. Spontaneous requesting for objects (IC-R) significantly increased following training (RR = 2.17, 95% CI [1.75, 2.68], p < .001), but requesting for social routine or commenting (IC-D) did not (RR = 1.34, 95% CI [0.83, 2.18], p = .237). Children in the ITG (n = 26) were observed again at follow-up (approximately 9 months after the end of the training period). Although the effect on spontaneous initiation using speech/vocalization (IC-S) had persisted (RR = 1.70, 95% CI [1.12, 2.58], p = .012), none of the other effects were significant (IC-P, RR = 0.69, 95% CI [0.41, 1.15], p = .15; IC-PS, RR = 1.90, 95% CI [0.76, 4.76], p = .17; IC-R, RR = 1.11, 95% CI [0.76, 1.62], p = .60).
Variables moderating the effect of PECS training
We analyzed baseline variables for their power to predict progress in general and to predict specific response to treatment. Seven baseline variables (shown in bold in Table 2) were independently and significantly related to general progress at postintervention and so were testable as potential moderators of the intervention effects. Of these seven Intervention × Baseline Variable interactions tested, two were found to be significant. The impact of the intervention on children's spontaneous initiation of communication using speech/vocalization (IC-S) was moderated by baseline autistic symptomatology (RR = 0.90, 95% CI [0.83, 0.98], p = .011). As can be seen in Figure 2, children whose autistic symptomatology score was lowest at baseline (i.e., least severe symptoms) showed the largest increases in spontaneous use of speech/vocalization following intervention. Each unit increase in ADOS score was associated with a 10% decrease in average rate of initiation using speech/vocalization (IC-S). Baseline expressive language did not moderate intervention effects for this outcome (RR = 1.05, 95% CI [0.90, 1.24], p = .524).
Figure 2. Graph showing the moderating effect of autistic symptomatology on the effect of PECS on children's spontaneous communication using speech/vocalization (Autism Diagnosis Observation Schedule [ADOS] scores are on a severity scale; higher score means more severe symptomatology).
Baseline expressive language moderated the effect of PECS training on children's spontaneous initiation using picture cards and speech/vocalization together (RR = 0.62, 95% CI [0.44, 0.88], p = .008). Expressive language was rated on a severity scale. Figure 3 shows that those children with the most expressive language at baseline (lower score represents better expressive language) showed the biggest increase in their use of picture cards and speech/vocalization together to spontaneously initiate communication. Each unit increase in expressive language deficit score was associated with a 38% decrease in the average rate of initiations using picture cards and speech together (IC-PS). Neither baseline developmental quotient nor autistic symptomatology moderated the effects of the intervention for this outcome (RR = 1.01, 95% CI [0.98, 1.05], p = .539 and RR = 0.94, 95% CI [0.79, 1.12], p = .477, respectively). As can be seen in Table 2, baseline developmental quotient (DQ) predicted of rate of initiation using picture card (IC-P) and rate of initiation for the purpose of instrumental requesting (IC-R) immediately postintervention, but interaction tests demonstrated that this did not moderate the effects of the training on these behaviors (for IC-P, DQ × Intervention, RR = 0.99, 95% CI [0.98, 1.01], p = .214; for IC-R, DQ × Intervention, RR = 0.99, 95% CI [0.98, 1.00], p = .212).
Figure 3. Graph showing the moderating effect of baseline expressive language on children's spontaneous communication using picture cards and speech/vocalization. ADOS = Autism Diagnosis Observation Schedule.
Discussion
PECS is recognized as an effective intervention for increasing communication in children with autism (Preston & Carter, 2009; Sulzer-Azaroff et al., 2009), and our RCT demonstrated specifically that PECS training can significantly enhance the spontaneity of children's communication (Howlin et al., 2007). In this article, we asked exactly how PECS training increased this communicative spontaneity and for which children. That is, we wanted to examine, first, whether the increased spontaneity was confined to communication using the picture symbols or whether PECS also impacted on the spontaneity of children's use of speech/vocalization. Second, we wished to examine whether the increased spontaneous communication was being used only for instrumental purposes (e.g., getting a snack) or whether children were also spontaneously initiating communication for more social purposes as a result of PECS training. Third, we wanted to identify factors that might be moderating the effect of PECS training and therefore predictive of which children might benefit most from the training. We used Poisson regression analysis to examine the children's spontaneous communication using different communication modalities and for different functions and to test for interactions between the intervention and baseline child variables.
The naturalistic and relatively fine-grained outcome measurement meant that it was possible to analyze exactly how PECS was enhancing children's spontaneous communication in an everyday situation. A small number of previous intervention studies have examined the form of children's communication but have not focused purely on spontaneous unprompted communication. The present analyses revealed that although PECS training did lead to children spontaneously communicating more using the picture cards, it also led to increased spontaneity in children's use of speech and their use of picture cards and speech in combination. The training appears to have increased spontaneous requesting for objects or help but not spontaneous requesting for social routine or commenting.
In contrast to some other reports (Bondy & Frost, 1994; Charlop-Christy et al., 2002), in our primary analysis of PECS RCT, we did not observe an effect of the intervention on overall use of speech (Howlin et al., 2007). The present analysis revealed, however, that PECS did enhance the use of speech as a modality to spontaneously initiate communication, as well as enhancing spontaneity using picture cards. So, although it would appear that PECS training did not enhance speech development per se, for those children who were already using some speech or vocalization, PECS appears to have provided a structure for them to use this mode to communicate without prompting. It would seem that PECS fostered spontaneity more generally across modalities rather than just acting to increase children's use of picture cards. Furthermore, the effect of PECS training on children's spontaneous speech/vocalization appears to have been particularly robust as it was also observed 9 months after the end of the training period in the group who received PECS training early on. There was no long-term effect on spontaneous use of picture cards.
Detailed analysis of the functions of spontaneous communication in autism intervention is also rare. In this study, analysis revealed a clear effect on children's spontaneous communication for the purposes of requesting for objects, such as a drink or a toy, which is the first communicative function taught through PECS teaching phases (Frost & Bondy, 2002). This replicates findings from earlier research (Schwartz et al., 1998; Yoder & Stone, 2006a, 2006b). There was no effect of training on children's spontaneous communication for social purposes. This might be due to the fact that the children in this sample had severe autism symptoms and, as a group, were very delayed with regard to verbal and nonverbal skills. Furthermore, the discrepancy between instrumental and social communication is perhaps to be expected given that the children were observed in class snack sessions. It is possible that observation of children in other nonsnack sessions might have revealed effects of training on communication for other noninstrumental purposes. Also, it is possible that if the training had persisted for longer or had been more intense, changes in spontaneous social, noninstrumental communication might have been seen. Some case study reports have described children successfully learning to communicate for social interaction purposes such as commenting (e.g., Schwartz et al., 1998; Webb, 2000), although, to date, no experimental trials have demonstrated this effect of PECS.
Two baseline variables appeared to moderate the effect of PECS training. First, less severe autistic symptomatology at baseline predicted the greatest increases in spontaneous speech. Second, higher level of expressive language at baseline predicted greater increases in spontaneous use of speech and picture cards together. This is to be expected, as more severe autism and greater language disability are not independent. Thus, the fact that the least severely autistic children and those with the most expressive language showed the greatest improvements in these areas is consistent with the autism intervention literature more generally (e.g., Harris & Handleman, 2000; Kasari et al., 2008). We observed no interactions between PECS training and any of the abilities measured at baseline on children's spontaneous use of the picture cards or spontaneous requesting. Yoder and Stone's (2006a,2006b) studies have been the only other systematic examination of moderators of PECS intervention. They compared PECS with RPMT, and although there was no overall difference between the interventions, children who were most impaired in baseline language and joint attention skills gained most in terms of their joint attention skills from PECS training, whereas the more able children made better progress with RPMT. The present study did not replicate the finding that the less able children benefited more from PECS. A potential explanation is that, as a group, the children in the present sample were less able than Yoder and Stone's sample. In Yoder and Stone's sample, the mean nonverbal mental age was 18.8 months (standard deviation 4.5 months) at 3 years of age (Table 1 in Yoder & Stone, 2006a, p. 429), meaning that the mean NVDQ was approximately 50, whereas the mean NVDQ in our sample was around 30.
Despite the fact that all children in the present study were very impaired in terms of their verbal and nonverbal skills, spontaneous use of pictures to communicate and spontaneous requesting did increase, and this was not predicted by better baseline language or less severe autism symptoms. This suggests that PECS training was equally accessible to these children in terms of teaching these skills specifically. This seems to support the idea that, beyond the need for some very basic cognitive skills required in order to exchange the cards (e.g., object permanence), few preexisting verbal or nonverbal skills are required to learn to use PECS (Bondy & Frost, 1998).
Strengths and limitations of the present study
The unique quality of the data presented here is that they are derived from an examination of “real world” effectiveness. The study took an inclusive approach to recruitment, aiming to include all suitable schools within a defined but large geographical area in the south east of England and included all suitable children whose parents consented. As the trial was community based, intervention was delivered to teachers and classroom staff via a workshop and follow-up visits to the schools. Teachers had to implement the program amidst all the other pressures and distractions of running a classroom for children with special educational needs and from a wide variety of backgrounds. Children were required to access the intervention in spite of their severe autistic symptoms, language impairments, and perhaps other comorbid problems. In other words, PECS training that was delivered and evaluated seemed to be a realistic representation of PECS training most children are likely to receive.
The design of the study and analysis ensured that the use of three treatment groups did not detract from the numbers effectively used for the intervention and no-intervention groups. The study was, in fact, strengthened by having within-individual comparisons (i.e., the delayed intervention group) as well as between-group comparisons over the same time frame. The use of the multilevel model allowed for efficient use of data in the three-treatment arm format adopted in this study, taking into account correlations between repeats from the same individuals and allowing for the serial nature of measurements under different treatment conditions. The design also enhanced the power to investigate the immediate effects of the intervention and enabled investigation of the longer term effects of the training. The incorporation of baseline data further strengthened the results.
The interaction analysis applied in this study is relatively novel to this field and demonstrates the possibility of using relatively sophisticated statistical models to test for moderator effects on interventions for children with autism. As has been discussed above, this has been rarely done in the autism field in the past, and thus there is very little reliable information about who benefits most from various interventions for children with autism.
As this trial was conducted primarily in schools, we had little direct contact with parents, aside from their consenting for their child to take part in the study. As a consequence, we did not collect systematic information on family variables such as ethnicity and other background factors (socioeconomic status, parental income) that might also be related to differential outcome, nor did we collect detailed information on potential school moderating factors. Instead, we focused on child characteristics as moderating factors. Generalization of these findings will require replication in samples with well-described demographic information as well as well-characterized schools/classrooms.
For logistical reasons (i.e., limited resources), we were limited to observing children in their classrooms. We opted to observe them during snack sessions as this was a session that created more opportunities for children to make spontaneous requests, relative to less structured sessions, and this was a common feature on the timetables of all classes involved in the study. However, the snack sessions are relatively brief periods, when children are usually highly motivated to make approaches for food, and so the data may not represent changes in children's communication in other less structured or less motivating contexts. Observations of children communicating in other class sessions or at home would have revealed the extent to which the observed effects generalized out of the relatively structured setting of class snack time.
With regard to the analysis of intervention response predictors, we were limited to testing four child factors measured at baseline. It is possible that other factors, not measured, were moderating the intervention effects, including those that were external to the children (i.e., environmental factors). It is likely that differences between the classes and the ways in which PECS was implemented also influenced children's progress. Treatment fidelity measures will be important for future studies of psychosocial interventions.
A limitation of the analysis of response predictors was that, although relatively sophisticated, essentially it was based on comparing subgroups, and the study was not primarily powered for this. The chance of Type II errors is thus increased. The results do, however, provide a good basis for further discussion. In the future, as more intervention studies are conducted and there is greater consistency in approach across research, pooling of samples may be possible, thus increasing statistical power for identifying invention response moderators.
Implications
In summary, the findings show that classroom-based PECS training enhances children's ability to make spontaneous instrumental requests not only using pictures but also using speech, or a combination of both. It also shows that, similar to other interventions, less impaired children appear to show the most improvement in these areas. Where these improvements were seen, they represented noticeable change in children's communication. For example, in one treatment group, the median rate of spontaneously initiated communication using PECS went from 0 times per 15-min snack session up to more than 6 times, and the median rate of spontaneous requesting rose from ~0.5 times to ~7 times per snack session. It is important to remember, however, that these figures are based on group effects. Such impressive gains were not seen in all children who received PECS training, and for some children, no gains were made at all. Nevertheless, for a child who has not been communicating at all to request even twice in a 15-min snack session represents a meaningful change.
The study also has important methodological implications. This article builds on the findings of one of the larger RCTs conducted in the autism field to date (Howlin et al., 2007; though see Aman et al., 2009; Green et al., 2010, for larger studies). The adequately sized sample, well described in terms of verbal and nonverbal abilities, provides the opportunity for analysis of the specific effects of psychosocial intervention for children with autism. The study demonstrates the feasibility of applying robust statistical techniques to pragmatic, “real world” trials in this field to elucidate exactly what changes and for whom.
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Submitted: March 25, 2010 Revised: December 28, 2010 Accepted: April 20, 2011
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Source: Journal of Consulting and Clinical Psychology. Vol. 79. (4), Aug, 2011 pp. 447-457)
Accession Number: 2011-15510-002
Digital Object Identifier: 10.1037/a0024379
Record: 3- Title:
- A meta-intervention to increase completion of an HIV-prevention intervention: Results from a randomized controlled trial in the state of Florida.
- Authors:
- Albarracín, Dolores. Department of Psychology and Marketing, University of Illinois at Urbana Champaign, Champaign, IL, US, dalbarra@illinois.edu
Wilson, Kristina. Florida Department of Health in Duval County, Jacksonville, FL, US
Durantini, Marta R.. Department of Psychology, University of Illinois at Urbana Champaign, Champaign, IL, US
Sunderrajan, Aashna. Department of Psychology, University of Illinois at Urbana Champaign, Champaign, FL, US
Livingood, William. Department of Office of the Dean, College of Medicine, University of Florida, Jacksonville, Jacksonville, FL, US - Address:
- Albarracín, Dolores, Department of Psychology and Marketing, University of Illinois at Urbana Champaign, 603 East Daniel Street, Champaign, IL, US, 61820, dalbarra@illinois.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 84(12), Dec, 2016. pp. 1052-1065.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 14
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- HIV, intervention, persuasion, retention, health promotion, randomized controlled trial
- Abstract (English):
- Objective: A randomized control trial with 722 eligible clients from a health department in the State of Florida was conducted to identify a simple, effective meta-intervention to increase completion of an HIV-prevention counseling program. Method: The overall design involved 2 factors representing an empowering and instrumental message, as well as an additional factor indicating presence or absence of expectations about the counseling. Completion of the 3-session counseling was determined by recording attendance. Results: A logistic regression analysis with the 3 factors of empowering message, instrumental message, and presence of mediator measures, as well as all interactions, revealed significant interactions between instrumental and empowering messages and between instrumental messages and presence of mediator measures. Results indicated that (a) the instrumental message alone produced most completion than any other message, and (b) when mediators were not measured, including the instrumental message led to greater completion. Conclusions: The overall gains in completion as a result of the instrumental message were 16%, implying success in the intended facilitation of counseling completion. The measures of mediators did not detect any experimental effects, probably because the effects were happening without much conscious awareness. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Impact Statement:
- What is the public health significance of this article?—This study shows that presenting a video that connects HIV-prevention counseling with outcomes and services that are important to clients (e.g., access to information about jobs, access to unrelated health services, opportunities to discuss emotional concerns) at the end of the first session increases completion of a 3-session counseling program. Treatment completion enhances outcomes in many domains, including HIV prevention. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Counseling; *Health Promotion; *HIV; *Intervention; *Retention; Persuasive Communication; Prevention
- PsycINFO Classification:
- Health & Mental Health Treatment & Prevention (3300)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Number of Sexual Partners Measure
Alcohol Use Measure
Drug Use Measure
Injection Drug Use Measure
Intentions to Use Condoms Measure
Condom Use and Unprotected Sex Measure DOI: 10.1037/t58419-000 - Clinical Trial Number:
- NCT01152281
- Methodology:
- Clinical Trial; Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Oct 27, 2016; Accepted: Jul 4, 2016; Revised: May 9, 2016; First Submitted: Nov 3, 2015
- Release Date:
- 20161027
- Correction Date:
- 20170417
- Copyright:
- American Psychological Association. 2016
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/ccp0000139
- PMID:
- 27786499
- Accession Number:
- 2016-51676-001
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2016-51676-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2016-51676-001&site=ehost-live">A meta-intervention to increase completion of an HIV-prevention intervention: Results from a randomized controlled trial in the state of Florida.</A>
- Database:
- PsycINFO
A Meta-Intervention to Increase Completion of an HIV-Prevention Intervention: Results From a Randomized Controlled Trial in the State of Florida
By: Dolores Albarracín
Department of Psychology and Marketing, University of Illinois at Urbana Champaign;
Kristina Wilson
Florida Department of Health in Duval County, Jacksonville, Florida
Marta R. Durantini
Department of Psychology, University of Illinois at Urbana Champaign
Aashna Sunderrajan
Department of Psychology, University of Illinois at Urbana Champaign
William Livingood
Department of Office of the Dean, College of Medicine, University of Florida, Jacksonville
Acknowledgement:
Retention and completion are critical components of the effectiveness of HIV-prevention interventions in real-world conditions and have established psychological determinants, such as attitudes and intentions (Albarracín, Durantini, Earl, Gunnoe, & Leeper, 2008; Noguchi, Albarracín, Durantini, & Glasman, 2007). Increasing retention is vital for public health because multisession behavioral interventions to reduce HIV risk are often more efficacious than single-session ones (Albarracín et al., 2005; Crepaz et al., 2014; Johnson et al., 2009; Meader et al., 2013). For example, the positive behavior change elicited by HIV-prevention interventions for clients of STI clinics is d = 0.33 for multisession programs, but only d = 0.06 for single-session programs (analyses of the data from Albarracín et al., 2005). However, when tested under conditions similar to the ones that are likely during actual implementation (e.g., lack of payments or other incentives), these multisession interventions show relatively low retention (Noguchi et al., 2007). Specifically, with the exception of interventions with captive audiences (e.g., prisons, inpatients), which show 100% completion rates, experimental interventions show a rate of completion of approximately 50% for participants initially enrolled (Branson, Peterman, Cannon, Ransom, & Zaidi, 1998; McMahon, Malow, Jennings, & Gómez, 2001). Without high retention, HIV-prevention interventions have less of an impact on behavior and clinical outcomes. Estimated associations for behavior change show that interventions with less than 50% retention rates produce a long-term decrease in HIV-safe behavior (d = −0.29), compared with an increase in HIV-safe behavior (d = 0.41) for those with 100% retention rates (Johnson et al., 2009). The present research examined the efficacy of two simple, postsession, messages to increase retention in a three-session risk-reduction counseling program. These messages were designed to either empower clients as agents responsible for their own change or highlight the instrumental outcomes of the intervention in terms of participants’ lives (e.g., addressing health concerns other than HIV, offering employment related information). The experimental design included five conditions, namely each of these messages, a combination of both, as well as two control conditions. The outcome variable was completion of a three-session counseling program.
Ensuring Retention in HIV-Prevention Programs Variability in Exposure to Behavioral Intentions
A number of interventions have been produced to change behaviors that place people at risk for HIV (Albarracín et al., 2005; Centers for Diseases Control and Prevention [CDC], 2007; Lorimer et al., 2013). These interventions are typically tested under conditions that ensure the validity of the outcome assessments (Cook & Campbell, 1979). Thus, researchers try to involve community members to see if a particular intervention works for them. Social networks are called upon to recruit these participants and numerous incentives and facilitators are used to ensure access to the desired sample of exposed participants, as well as low attrition (De Walque et al., 2012; Exner, Hoffman, Parikh, Leu, & Ehrhardt, 2002; Lauby et al., 1996; Linnan et al., 2002; Packel et al., 2012; Rabinowitz, 2002; Raj et al., 2001; Roffman, Picciano, Bolan, & Kalichman, 1997; Schilling & Sachs, 1993; Schweitzer, 1997; Tobias, Wood, & Drainoni, 2006). Although these procedures are necessary to determine whether a program works for an exposed population (efficacy trial), they remove the reluctance to participate when the intervention is implemented (Catania, Gibson, Chitwood, & Coates, 1990; Lauby et al., 1996; Packel et al., 2012). Contemporary research must thus address the fundamental scientific problem of variability in exposure to behavioral interventions, including completion of a program designed to elicit behavioral or medical change.
Despite the above-mentioned method of removing selection and attrition during tests of intervention efficacy, in real-world conditions, people choose to take part in preventive interventions (Albarracín et al., 2008; Condelli, Koch, & Fletcher, 2000; DiFranceisco et al., 1998; Hennessy, Mercier, Williams, & Arno, 2002; Noguchi et al., 2007; Rutledge, Roffman, Picciano, Kalichman, & Berghuis, 2002; Veach, Remley, Kippers, & Sorg, 2000; Wagenaar et al., 2012). Given limited time and interest, clients of health facilities can accept or refuse to take part in an HIV-prevention counseling session (Albarracín et al., 2008; Grady, Kegeles, Lund, Wolk, & Farber, 1983; Katz et al., 2015; Noguchi et al., 2007; Minder, Müller, Gillmann, Beck, & Stuck, 2002; Wilson & Albarracín, 2015). Moreover, some of the audiences most vulnerable to HIV are the least likely to complete HIV-prevention interventions (Earl et al., 2009; Liu et al., 2014; Noguchi et al., 2007; Wilson & Albarracín, 2015; Yancey, Ortega, & Kumanyika, 2006). In particular, frequent condom users are more likely to complete pro–condom-use interventions than infrequent ones (Earl et al., 2009; Noguchi et al., 2007). Thus, efficacious interventions may not reach the vulnerable audiences in need of interventions.
Given that interventions need to fully reach vulnerable audiences, not just willing ones, it is imperative to develop and test procedures that increase participation by these populations (Albarracín et al., 2008; Wilson & Albarracín, 2015). Procedures can be designed to change an audience’s behavior with respect to the preventive interventions themselves, including enrollment and retention. These procedures, termed meta-interventions, entail a standardized introduction or context change (e.g., delivery setting) intended to increase exposure to a behavioral intervention (Albarracín et al., 2008; Albarracín, Leeper, Earl, & Durantini, 2008; Wilson, Durantini, Albarracín, Crause, & Albarracín, 2013). In past research, participants with prior infrequent condom use were offered an HIV-counseling session using one of four scripted introductions to the program (Albarracín et al., 2008). A randomly assigned meta-intervention conveying that counseling participants are free not to change (empowering video) was more effective than other introductions (one promising change and another providing basic information about the counseling) or no introduction (just an offer to take part). Unobtrusive observers recorded the extent to which participants agreed to the counseling when asked, and also collected supplementary data on participants’ reading of brochures and viewing of videos. As hypothesized, the empowering meta-intervention produced high levels of enrollment in the counseling (Albarracín et al., 2008). In addition, viewing the video had an independent effect on enrollment, such that viewers of the video were more likely to enroll in counseling than nonviewers (Albarracín et al., 2008).
Selective Exposure to Interventions
Retention in HIV-prevention interventions can be understood as a form of selective exposure to information (Albarracín & Mitchell, 2004; Earl & Nisson, 2015; Noguchi et al., 2007). Selective exposure comprises biased information seeking behavior and was first studied by Festinger (1964; for reviews see Eagly & Chaiken, 1993; Freedman & Sears, 1965; Frey, 1986). Exposure to an intervention (in this case, staying in and completing a program) depends on two sets of motivations (i.e., goals or desired endstates; Lewin, 1926; see also Atkinson, 1964; McClelland, 1951; Nuttin, 1980; for other classifications of human motives, see Chaiken, Wood & Eagly, 1996; Eagly, 2007; Johnson & Eagly, 1989; Noguchi et al., 2007; Prislin & Wood, 2005; Wyer & Albarracín, 2005). On the one hand, individuals are motivated to achieve subjective self-validation, which comprises the defense of prior beliefs and practices in the domain of HIV prevention (Albarracín & Mitchell, 2004; Albarracín et al., 2008; Noguchi et al., 2007; see also Kunda, 1990; Molden & Higgins, 2005). On the other hand, individuals are motivated to maximize objective outcomes, such as reducing their risk for HIV and achieving other personal and emotional outcomes (Hart et al., 2009; Noguchi et al., 2007; Vanable et al., 2012).
A primary human motive is to achieve self-validation (Chaiken, Wood & Eagly, 1996; Eagly, 2007; Johnson & Eagly, 1989; Prislin & Wood, 2005; Wyer & Albarracín, 2005), and interventions may or may not fulfill it (Albarracín et al., 2008; Noguchi et al., 2007). Presumably due to the self-validation motive, individuals who engage in high-risk behavior are reluctant to enroll and stay in HIV-prevention interventions (Albarracín et al., 2008; Earl et al., 2009; Noguchi et al., 2007; Wilson & Albarracín, 2015).
Considering this, it is possible to design empowering messages to decrease defensiveness when recipients encounter a potential intervention that urges novel or even rejected practices (e.g., using condoms for nonusers; Albarracín et al., 2008). For example, past research has found an advantage in telling participants that change is up to them, that an intervention will simply open doors, and that they may or may not change if they participate. This type of meta-intervention puts recipients in a more active role by placing the burden of change upon them, while indirectly encouraging them to actively seek change (Amaro, 1995; Amaro & Raj, 2000; Albarracín et al., 2008; Freire, 1972; Higa, Marks, Crepaz, Liau, & Lyles, 2012; Putnam, 1911). Further, people are more likely to expose themselves to persuasive communications if they believe that they can resist their influence (Albarracín & Mitchell, 2004; Brehm, 1972; Brehm & Cohen, 1962; Watzlawick, 1978). For example, as infrequent condom users often do not want to use condoms (Albarracín, Johnson, Fishbein, & Muellerleile, 2001), highlighting the option of resistance increases their exposure to condom-use interventions (Albarracín et al., 2008). These processes have been investigated to improve enrollment in HIV programs (Albarracín et al., 2008), but not to achieve completion. As the dynamic of enrollment is similar to retention (Noguchi et al., 2007), similar messages may also increase retention in an HIV-prevention counseling program.
Besides self-validation, an important human motive is to maximize objective outcomes (Hart et al., 2009; Noguchi et al., 2007). Retention in an intervention is therefore likely to depend on the degree to which the intervention fulfills this motive (Albarracín et al., 2008; Earl et al., 2009; Noguchi et al., 2007; Vanable et al., 2012). For HIV-risk reduction interventions, objective outcomes include HIV-risk reduction (Floyd, Prentice-Dunn, & Rogers, 2000; Rosenstock, Strecher, & Becker, 1994), but also emotional and instrumental support (Durantini & Albarracín, 2009; Vanable et al., 2012). For people who engage in high-risk behavior, the risk-reduction outcome can conflict with the self-validation motive (Albarracín et al., 2008; Earl et al., 2009; Noguchi et al., 2007). Thus, emphasizing that the objective of an intervention is to change participants’ risk behavior can lead participants to reject the intervention and feel manipulated (Albarracín et al., 2008). In contrast, emphasizing the emotional, social and instrumental value of an intervention beyond HIV prevention can entice participation (Durantini & Albarracín, 2007, 2009; Liu et al., 2014). Past research supports this assertion, showing that, women seek out programs that provide social and emotional support (i.e., company, encouragement, and affection), whereas men seek out programs that provide instrumental support (i.e., health care or payments; Durantini & Albarracín, 2007, 2009). In this light, we tested the effect of messages that emphasize the emotional, instrumental, and (non-HIV related) physical health outcomes of returning to the sessions of an HIV-prevention and counseling program.
HIV Prevention in the State of FloridaAt the end of 2012, an estimated 1,218,400 people in the United States were living with HIV/AIDS (Hall et al., 2015). HIV incidence has remained relatively stable since the mid-1990s, with an estimated 50,000 persons becoming infected with HIV on any given year (Hall et al., 2008). Based on confidential-name–based HIV reports, 47,352 cases of HIV/AIDS were diagnosed in 35 U.S. areas (33 states, Guam, and the U.S. Virgin Islands) in 2013. Up to 2012, the cumulative number of individuals dead by HIV was 658,507, with Florida having one of the highest HIV disease death rates in the U.S. (Florida Department of Health [FDH], 2012). Also, in 2013, Florida ranked first in new infections per year (5,377 new infections) and second in number of cumulative reported HIV cases (49,058; CDC, 2013). In 2014, the largest estimated proportion of HIV/AIDS diagnoses in Florida was for men who have sex with men (MSM), and ethnic minority adults and adolescents infected through heterosexual contact (FDH, 2015). Clearly, protecting Floridians is a national health priority, particularly those from African American backgrounds, who are highly represented in our population.
In Florida, prevention is the most important tool to avoid an even more accelerated epidemic (FDH, 2007). Duval County (Electoral district 4) is an important area that has received relatively little research attention (compared with Dade County, e.g.). Considering sheer number of cases in 2014, Duval County, which includes Jacksonville, ranks 1st for Gonorrhea and 4th for Chlamydia (FDH, 2014a), and 4th for HIV (FDH, 2014b). Of 67 counties in the state of Florida, these rankings place the region at very high risk. Given these findings, ensuring intervention effectiveness for this population is key.
The Present ResearchA randomized control trial was used to test the impact of video meta-interventions designed to either empower clients or remind them of the various objective goals fulfilled by the HIV-counseling program, and to compare these videos with control videos. The empowering meta-intervention entailed presenting the recipient as the motor of the behavior change (Albarracín et al., 2008). This strategy emphasized that the program could not change behavior unless the individual wanted it to. The instrumental video included descriptions of the sort of information and referrals the counselor could provide, in addition to giving information and guidance about HIV prevention. There was also a condition that combined the empowering and instrumental messages, as well as two control conditions. One control included stories about people living with HIV that were used in all experimental videos. The other control was more minimal, and simply presented educational information on reducing STIs. Thus, the design comprised five conditions to analyze their impact on completion of a CDC-recommended, three-session counseling program.
Our design also included a factor signaling whether perceptions of the video were measured. Although it was important to attempt to measure whether the videos induced expectations of empowerment and instrumental outcomes, including such blunt measures often alters the outcomes of experimental designs (Dholakia & Morwitz, 2002; Morwitz, Johnson, & Schmittlein, 1993). As a compromise, we randomized whether these measures appeared, and so only half of the sample completed these measures immediately after watching the meta-intervention video.
Method Enrollment
Clients from the STI clinics from the Florida Department of Health in Duval County were recruited (via flyers, referrals) for a study testing a three-session counseling program. To be eligible, individuals had to be between 18 and 35 years of age, report engaging in sexual activity in the past three months, and report using condoms never or occasionally. Participants were excluded if they were HIV-positive, or were trying to get pregnant or had a partner who was trying to get pregnant. Eligible participants were scheduled for their first study appointment. To ensure initial enrollment, participants were paid $35 for attending the first session, and $15 for subsequent sessions. The study was approved by the Institutional Review Boards (IRBs) of the University of Illinois, University of Pennsylvania, and Florida Department of Health, and each participant provided informed consent. Figure 1 describes all exclusions and Ns resulting from assignment procedures. The maximal control was by design smaller than the other conditions, including the minimal control. The trial was preregistered in clinicaltrials.gov (NCT01152281).
Figure 1. Recruitment and assignment.
Participants
Seven hundred twenty-two eligible participants (58% female) attended the initial counseling session, with a retention rate of 76% for the second session and a completion rate of 63% at the third session. The mean age of the sample was 26.54 (SD = 4.78). The majority of participants were African American (79%), and generally had an income under $9,999 per year (58%). Eighty-five percent reported having a main partner with whom they had a relationship on average of 2.37 years (SD = 2.10). Condom use in this sample was low, with only 1.1% reporting always using a condom when they had sex with their main partner. A full description of the same appears in Table 1.
Sample Description
The Counseling Intervention
The model of counseling that was used entailed a client-centered, cost-effective HIV-prevention program (CDC, 1993, 2007; Holtgrave, Valdiserri, Gerber, & Hinman, 1993; Kamb et al., 1998) facilitated by a counselor. This model’s efficacy has been demonstrated to significantly reduce STIs in a large multisite study (Kamb et al., 1998) and continues to be recommended as a standard for one-on-one counseling (CDC, 2007). The counseling seeks to reduce HIV risk behaviors by giving information, identifying risk behaviors, as well as steps to change them, and developing behavioral skills enabling safer behavior. This counseling can involve one or more sessions lasting at least 20 min, all of them following the same format. In our proposed study, a three-session model was used.
During the first session, participants received information regarding HIV transmission and prevention tailored to their culture, language, sex, gender, age, and educational level. The counselor ensured that the participant understood the information and that all of their misconceptions were corrected. The participant was encouraged to ask questions and clear their doubts. Following the informative part of the session, the counselor performed a personalized risk assessment, encouraging the participant to identify, understand, and acknowledge the behaviors and circumstances that put them at risk for being infected by HIV. Addressed topics included factors associated with risk behavior, such as using drugs or alcohol before sex, underestimating personal risk, having low self-efficacy, having distorted or fatalistic beliefs, and misperceiving peer norms. In addition, the counselor examined previous attempts made by the participant to reduce their risk and identified the reasons for their success or failure in these situations. This in-depth exploration allowed the counselor to help the participant consider ways to reduce personal risk and commit to a single, reachable step toward change. Once this risk assessment was complete, the counselor asked the participant to describe the risk-reduction step to be attempted (while acknowledging positive steps made), and helped the participant identify and commit to additional behavioral steps. Testing was also discussed, with referrals provided as needed.
During the following sessions, the counselor and the participant explored the success or failure of the steps proposed, and adjusted goals to the participant’s achievements. Furthermore, the second and third sessions also included a module for providing emotional support and addressing instrumental and/or medical concerns, in addition to HIV. This inclusion fulfilled the goal of providing supporting objective outcomes highlighted in some of the meta-intervention conditions. Among other things, after the HIV-risk reduction portion of the second and third counseling session was complete, the counselor discussed the physical and psychological symptoms, made referrals and provided information. This modification allowed us to test the effectiveness of messages that emphasized emotional and physical outcomes, with the counseling providing some venue for relief.
The counselors had good fidelity ratings using standard observation lists, and great high on cultural competency as measured with a valid and reliable questionnaire (Ponterotto, Alexander, & Grieger, 1995; Ponterotto, Potere, & Johansen, 2002). The counselors used written guides and records to ensure the use of a standardized procedure, and were closely supervised and retrained periodically. Therefore, after initial intensive training before the trial began, a check of videotaped sessions was performed to ensure proper application of the protocol. A random sample of 38 sessions showed 100% adherence to seven key dimensions of the protocol, which included appropriate introduction to the session, adequate performance of the risk assessment, proper evaluation of personal resources, proper evaluation of barriers, adequate integration of personal resources into newly set goals, and a clear closure. The average duration of the sessions was 25 min.
Meta-Intervention Messages
The messages were 24- to 34-min videos presented at the end of the first counseling session, to infer effects on retention at the second and third sessions. There were five videos, one for each condition, one resulting from crossing two meta-interventions, and two control videos.
The first experimental video, lasting 28 min, presented a meta-intervention conveying the message of being empowered (empowering condition). The video presented community members who talked about their experiences with HIV and counseling. This content was interspersed with messages delivered by these characters and professionals, conveying that subsequent counseling sessions were not intended to force change upon individuals. The stories were set at local places in North Florida (e.g., a fishing environment, a bar) with local music used in the background. The videos contained material in both Spanish and English, subtitled to the other language.
The second experimental video, lasting 26 min, presented a meta-intervention emphasizing the objective outcomes associated with HIV-prevention counseling (instrumental condition). This video presented the same stories as the first message. However, this experimental video also emphasized the emotional, social and objective (i.e., non-HIV/STI health) outcomes of returning to the counseling sessions. In this message, characters and professionals described how HIV-prevention counseling was also a venue to discuss personal problems, such as violence in the home or depression, and the extent to which many clients find emotional relief and social support from participating in the counseling. This message thus conveyed how HIV-prevention counseling often facilitates the treatment of other health problems, while also providing a venue for obtaining information about, and referral to, social services.
The third experimental video, lasting approximately 34 min, combined the first and the second meta-intervention messages. The final two videos were both control videos. The first, which lasted approximately 26 min, included the same stories and locations as those presented in the other three videos, but did not contain any of the meta-intervention messages (minimal control condition). The second, lasting 24 min, contained neither stories presented by local characters, nor any meta-intervention messages, but simply presented short vignettes aimed at increasing behavioral skills, perceived risk and knowledge about reducing STIs (maximal control condition; selected from video developed by Warner et al., 2008).
The use of two control conditions let us disentangle the effects of the meta-intervention messages from the community stories about HIV and counseling. Specifically, the difference between the minimal control video and those used in the three experimental conditions was the absence of a meta-intervention message; the rest of the content (e.g., the community stories) remained the same. The maximal control did not have either, and only presented risk and facts about STIs. Thus, it became possible to see whether differences in the three experimental conditions were attributable to the combination of the meta-intervention message and stories that were included, or were exclusive to the meta-intervention message. This condition was added after the project was funded and therefore had to be smaller because of funding constraints.
Design
This study crossed two meta-interventions: (a) a video message empowering or validating the client to return to the sessions, and (b) a video message highlighting opportunities for emotional and instrumental support (e.g., information about cardiovascular health, referrals etc.) facilitated by HIV-prevention counseling. The design had another factor, which concerned the inclusion of measures of expectation induced by the video, which were to appear immediately following the video. Only half of the sample completed these measures with the objective of avoiding measurement sensitivity. Thus, our design was a 2 (empowering meta-intervention: present vs. absent) × 2 (instrumental meta-intervention: present vs. absent) × 2 (measurement of mediators: present or absent) between-subjects factorial with the addition of a minimal control condition.
Baseline Measures
Data was collected using audio computer-assisted self-interview (ACASI) procedures. With this technique, participants listened to the question, while simultaneously reading them on the screen. ACASI procedures have been reported to increase accuracy with respect to non-normative behaviors and responses, thus decreasing the effects of social desirability and experimental demand (see, e.g., Des Jarlais et al., 1999; Mensch, Hewett, & Erulkar, 2003; Williams et al., 2000). Questionnaires were available in Spanish for participants who preferred it.
Baseline questionnaires measuring past behavior, intentions, and demographics were collected from participants before the start of their first counseling session. Questionnaire items were first transformed to a z-score, and then averaged, to produce a composite measure of condom, drug, alcohol and injection drug use, as well as a composite measure for number of sexual partners and intentions to use condoms.
Condom use and unprotected sex
Participants were asked about their condom use during intercourse in (a) the past month, (b) the past three months and (c) the past six months, as well as (d) how often they use condoms in general, (e) how many times they engaged in unprotected sex in the past six months, and (f) whether they used a condom the last time they had intercourse. These questions were asked in reference to participants’ main and other partner(s), and had acceptable internal consistency (α = .69 for main partner, and α = .63 for other partner).
Number of sexual partners
Participants were asked about the number of sexual partners they had in (a) the past 48 hours, (b) the past month, and (c) the past six months. This measure (National Institute of Drug Abuse [NIDA], 1991, 1993) had good internal consistency in our sample (α = .77; see also Edwards, Fisher, Johnson, Reynolds, & Redpath, 2007; Needle et al., 1995).
Alcohol use
Participants were asked to report their behaviors related to prior alcohol use. For those participants who reported that they drink alcohol, alcohol-use consisted of a single-item measure including reports of the number of times participants used alcohol during the past week.
Drug use
Participants were also asked to report their behaviors related to prior drug use. Drug-use measures included reports of the number of times participants used drugs (in general, as well as heroin, crack, and cocaine) during (a) the past 48 hours and (b) the past month. This measure had poor internal consistency in our sample (α = .52).
Injection drug use
Injection drug use was differentiated from the broader measure of drug use, as the level of HIV risk conferred by intravenous drug users is higher. Participants were asked (a) the number of times they injected drugs, (b) the frequency of sharing syringes, (c) the number of sharing partners, and (d) the number of times the equipment was sterilized between uses over a period of the past 48 hours, past month, and past six months. These questions were validated against HIV infection rates by Anthony et al. (1991), and had good internal consistency in our study (α = .85).
Intentions to use condoms
Participants were also asked about their intentions to use condoms, using previously validated measures (Albarracín et al., 2000; Earl et al., 2009; Kamb et al., 1998). Specifically, participants were asked how likely it was for them to use a condom with their partner (a) the next time they had intercourse, (b) every time for the next three months they had intercourse, and (c) every time for the next six months they had intercourse. Participants were also asked about (d) the strength of their intentions and (e) their motivation to use condoms with their partner in the next six months. These questions were asked in reference to participants’ main and other partner(s), and had excellent internal consistency (α = .94 for main partner, and α = .96 for other partner).
In addition to the above measures, participants were also asked standard items from the General Social Survey (http://gss.norc.org/) to assess structural variables, namely household income, level of education, race/ethnicity, and employment.
Measures of Video Acceptability, Counseling Expectations, and Return Intentions
The design included measures of the acceptability of the video, expectations of the following counseling sessions, and intentions to return. Measures were completed after the presentation of the video, by only half of the participants. Items for each measure were first transformed to a z-score, and then averaged, to create a composite measure for video acceptability, counseling expectations and return intentions.
To gauge video acceptability, participants were asked whether the video presented was (a) interesting, (b) useful, (c) enjoyable, (d) clear, and (e) relevant, as well as whether the video (f) made participants think, (g) taught them about condom use, and (h) presented new information. Participants were also asked whether the video made them (i) nervous, (j) worry, (k) feel compelled to do something they did not want to do, and (l) feel forced to change their beliefs or behaviors. Items (i) to (l) were first reverse scored, and then averaged with items (a) to (h). These measures had high internal consistency (α = .80).
Participants were also given measures of expectations about the counseling. Specifically, we asked participants whether they thought that the counseling would (a) force or (b) compel them to do things they did not like, (c) make them do things to please the counselor, (d) increase HIV safe behavior, (e) help them discuss health problems besides HIV and STIs, and (f) help them with their emotional concerns. Items (a) through (c) addressed empowerment expectations (α = .65), and items (d) through (f) addressed instrumental outcomes (α = .76).
Finally, participants were asked about the (a) strength of their return intentions and (b) how much they would enjoy returning (α = .67). All these measures were included as potential process data.
Completion Measure
Retention was observed during the last two sessions. When participants started the first session, the counselor indicated that the complete counseling program included two additional follow-up sessions. We measured retention by taking into account whether the participant completed all three sessions.
ResultsAcross the board, there was a high completion rate of 63%, which is probably attributable to the use of payments for attendance at the return sessions. Before analyzing the outcome of the meta-intervention, we compared the demographic and behavioral profile of our sample. Any incidental difference was then controlled for in the main analysis.
Comparability Across Conditions
Although random assignment was intended to ensure comparability across conditions, we performed periodic checks to make sure there were no gender, age or race biases in the participant distribution. Table 1 provides a summary of relevant sample characteristics, by overall sample, as well as broken down by condition. One-way ANOVAs and chi-square tests revealed no significant difference in these variables across our five conditions (ps > .077), with the exception of age and race. Variability in race across conditions approached significance, χ(18) = 28.71, p = .052. There was a significant difference in the age of participants across conditions, F(9, 712) = 2.15, p = .024. A Tukey post hoc test revealed that participants’ age was significantly lower in the combined instrumental and empowering meta-intervention condition, when no measures of return expectations and intentions were included (M = 25.08, SD = 4.46), compared with the same condition presented when those variables were measured (M = 27.52, SD = 5.06). There were no significant differences in age across the other conditions (ps > .089).
Effects on Video Acceptability, Counseling Expectations, and Return Intentions
A multivariate analysis of variance, with our five meta-intervention message conditions as a factor, was conducted to analyze the impact of our experimental factors on video acceptability, counseling expectations (either empowering or instrumental) and the intention to return to counseling. Results revealed no significant effect of meta-intervention message (p = .14), indicating that our experimental factor did not affect participants’ reported acceptability of the video, empowering or instrumental expectations of counseling, or their intentions to return to the next counseling session. These findings suggest that any effect of the video either occurred outside of awareness, or could not be clearly reported by our participants on the scales that we developed. Means and standard deviations for video acceptability, counseling expectations and return intentions are presented in Table 2. The means in all cases were above the midpoints of the scales and suggest favorable perceptions of the video and a program perceived to be acceptable.
Means and Standard Deviations for Video Acceptability, Counseling Expectations, and Return Intentions Presented Across Meta-Intervention Message Conditions
Main Experimental Results
A logistic regression analysis with our three factors of empowering message, instrumental message, and mediator measurement presence, as well as all interactions, was conducted to analyze the impact of our experimental factors on counseling completion. In this analysis, the two control conditions were combined but a separate consideration of these two conditions does not alter our results. The analyses entailed a forward removal of predictors. The results from this analysis appear in Table 3. Results revealed a significant two-way interaction between the presentation of instrumental and empowering messages, as well as a significant interaction between mediator measure presence and presentation of instrumental messages (see Figure 2). Results indicated that the instrumental message alone was better than any of the other messages. Furthermore, the instrumental message was more effective than the empowering message in the absence of measures of mediating expectations. The overall gains in completion as a result of the instrumental message were 16%, suggesting success in the intended facilitation of counseling completion.
Final Results From Logistic Regression Analysis
Figure 2. Effects of meta-interventions.
DiscussionThis paper reported a large and complex randomized controlled trial testing meta-interventions to increase completion of a CDC-recommended counseling for HIV prevention. Our results identified a successful program—one that incorporates the counseling within a broader spectrum of goals that are likely salient to the clients of most programs to prevent, test for, and treat HIV. This finding is particularly impressive given that the completion rates in the sample were fairly high, probably attributable to a combination of excellent counseling technique and highly effective counselors, in addition to the use of payments for follow-up sessions. In other words, the room for improvement may have been limited to begin with, or at least, limited relative to the usually lower completion rates in the average comparable HIV-prevention trial (see Albarracín et al., 2005). Also, although the use of payments seemed desirable given pilot data showing lower completion than that ultimately obtained, hindsight suggests that the payments might have reduced the sensitivity of our completion measure.
The instrumental meta-intervention seemed important to test as it involves a patient centered approach to interventions (Lauver et al., 2002; Morgan & Yoder, 2012; Robinson, Callister, Berry, & Dearing, 2008) that is entirely consistent with psychological theories of persuasion and motivation. For a message to be well received, it is necessary for its content to be relevant to the audience and sufficiently consistent to ensure high level of message-consistent thinking and low levels of counterarguing (Albarracín, Johnson, & Zanna, 2005; Albarracín & Vargas, 2010). In the case of the instrumental message, highlighting the various personal goals that can be met through contact with the health system and associated services clearly retained participants who otherwise may have dropped out from the program. Future research should be conducted to replicate this finding in other areas, particularly HIV testing, HIV treatment, and introduction of pharmacological agents, such as PrEP.
Three aspects of our findings are noteworthy. First, the empowering meta-intervention, which had impressive results in a trial to increase acceptance of HIV-prevention counseling (Albarracín et al., 2008), did not yield improved completion. This result highlights that the determinants of enrollment and retention are different, with defensiveness playing a key role in initiation, but lack of relevance or perceived purpose probably underlying drop out. Second, the average completion rate was rather high and so our meta-intervention may have stronger effects when completion is low to begin. In our case, the high quality of the counseling and intensive training and supervision of the counselors, along with the payments, decreased the need for an intervention to ensure completion. Replications in conditions that are more conducive to higher drop out will therefore be highly informative. Third, as is common in testing behavioral interventions, the mediation analysis shed no light on the variables that led to the treatment outcome. It is of course possible that expectations did change, but participants did not have full introspective access to these contents due to the operation of relatively nonconscious processes. More likely, however, the questions were too involved and required a level of metacognition that is unfortunately not frequent for a sample with a low level of education. Perhaps a less directive assessment, such as a qualitative interview, may in the future increase understanding of the reasons underlying the success of the instrumental message.
Effects of the Mediators
The introduction of the mediators was expected to affect completion by sensitizing clients to the importance of completion. Often, calling attention to what the goals of a study are can introduce demands effects (Barabasz & Barabasz, 1992). An interesting study on measurement effects, however, was conducted by Glasman and colleagues (2015), who found that introducing measurements of risky behavior decreased the effect of a prevention intervention, suggesting a potential underestimation of the effect of behavioral programs. Both the demand and efficacy reduction patterns are entirely consistent with what we found. The inclusion of mediator measures increased completion, while also decreasing sensitivity to the meta-intervention. It seems likely that in-depth questions elicit cognitive and motivational processes, such as self-talk, that distract recipients from fully processing messages received immediately before (Glasman et al., 2015) or after (in our study) receiving a persuasive communication.
Remaining Questions and Limitations
There are several important questions to address, including possible differences in the intervention as a function of the delivered meta-intervention. For all clients, during the second and third counseling sessions, counselors discussed the physical and psychological symptoms associated with HIV, addressed instrumental and medical concerns, and provided emotional support for the participants regardless of the condition they were randomized to. Thus, concern over the influence of a meta-intervention condition on counselors’ interactions with participants is mitigated by the fact that the delivery of the counseling and the delivery of the meta-intervention messages were done by different team members. The counselor was blind to the meta-intervention condition, and so, all subsequent interactions with participants could not have been biased by knowledge of experimental condition.
With respect to generalizability to the population, we restricted the sample of participants to 18- to 35-year-olds because the estimated number of diagnoses of HIV infections in the U.S. is highest for this age range (Center for Disease Control, 2014). Additionally, prior work has shown that the mean age of participants enrolling in HIV-prevention intervention programs falls within this range (e.g., Liu et al., 2014; Wilson, Durantini, Albarracín, Crause, & Albarracín, 2013). Despite this age range restriction, we do not believe the generalizability of the studies should be affected, as the meta-interventions used target broad psychological themes, such as seeking self-validation or maximizing objective outcomes, which are not limited to specific age groups.
With respect to generalizability to the intervention format, a three-session counseling program was selected both because previous work we have done showed three-session interventions were a good length (Liu et al., 2014), and because of cost considerations. In principle, it is possible that the meta-intervention messages used in this study might have different efficacy with a longer program. However, given that completion was very high to begin, a program with lower rates of completion may show a stronger effect of the meta-interventions. Furthermore, we limited the presentation of the meta-intervention messages to participants who attended the first session as we were interested in the effect of these messages on completion of a program after it starts. Prior work we have conducted has already addressed the benefits of certain meta-intervention messages on increasing enrollment in HIV-prevention intervention programs (Albarracín et al., 2008), where it made more sense to present these messages to the entire sample at baseline.
One important consideration is the potential effect of the financial incentive to participation used in this study. As is well known, paying individuals for performing a task can reduce the perception of freedom of choice, and in turn decrease intrinsic motivation for the task (see, e.g., Festinger, 1964). In this context, payments could have led to lesser motivation to complete the program than lack of payments. This possibility seems unlikely because of the very high completion rates we obtained in our study. A likely possibility, however, is that the payment might have decreased motivation for the empowering condition, which emphasized freedom of choice. For example, emphasizing that clients are active participants and the motor of change may have reminded them of the payment and thus reduce their motivation to complete the program. Thus, future work should test the meta-intervention in the absence of payments.
It is important to further consider the effect of the meta-interventions, particularly the fact that the empowering one seemed to offer no benefits. In this regard, although empowering messages have been effective at eliciting enrollment before the intervention is delivered, a retention meta-intervention of this type may be directly in conflict with the obvious behavior-change intent of the program. The preventive nature of the program is likely to be apparent from a first session in which participants are encouraged to identify, understand and acknowledge the behaviors and circumstances that put them at risk for being infected with HIV. Also, it seems possible that the video might become impractical in some contexts, particularly if it were excessively long. However, the counseling session lasted 25 min in average, which along with a 26-min instrumental video would result in a first session of 51 min of length. Thus, it seems possible to integrate this meta-intervention into the regular operation of a clinic.
Although efficacious interventions are key tools in the prevention, detection, and treatment of HIV, the public health impact of these interventions are likely reduced when vulnerable populations do not complete the program. Our findings provide evidence that a meta-intervention simply describing HIV-prevention counseling as a venue where one can discuss personal problems or medical needs, and receive appropriate referrals to community resources, appears to be a promising strategy for increasing retention. The development of cost-effective tools to retain clients in multisession HIV-prevention programs could have a significant impact on the lives of those at greatest risk for HIV infection and may play a pivotal role in decreasing the number of new HIV infections in Florida, and in the Nation.
Footnotes 1 Only one participant asked for the Spanish version of the measures.
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Submitted: November 3, 2015 Revised: May 9, 2016 Accepted: July 4, 2016
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Source: Journal of Consulting and Clinical Psychology. Vol. 84. (12), Dec, 2016 pp. 1052-1065)
Accession Number: 2016-51676-001
Digital Object Identifier: 10.1037/ccp0000139
Record: 4- Title:
- A method for making inferences in network analysis: Comment on Forbes, Wright, Markon, and Krueger (2017).
- Authors:
- Steinley, Douglas. Department of Psychological Sciences, University of Missouri, Columbia, MO, US, steinleyd@missouri.edu
Hoffman, Michaela. Department of Psychological Sciences, University of Missouri, Columbia, MO, US
Brusco, Michael J.. Department of Business Analytics, Information Systems & Supply Chain, Florida State University, Tallahassee, FL, US
Sher, Kenneth J.. Department of Psychological Sciences, University of Missouri, Columbia, MO, US - Address:
- Steinley, Douglas, Department of Psychological Sciences, University of Missouri, 210 McAlester Hall, Columbia, MO, US, 65211, steinleyd@missouri.edu
- Source:
- Journal of Abnormal Psychology, Vol 126(7), Oct, 2017. pp. 1000-1010.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- network analysis, Monte Carlo tests, multivariate binary data
- Abstract (English):
- Forbes, Wright, Markon, and Krueger (2017) make a compelling case for proceeding cautiously with respect to the overinterpretation and dissemination of results using the increasingly popular approach of creating 'networks' from co-occurrences of psychopathology symptoms. We commend the authors on their initial investigation and their utilization of cross-validation techniques in an effort to capture the stability of a variety of network estimation methods. Such techniques get at the heart of establishing 'reproducibility,' an increasing focus of concern in both psychology (e.g., Pashler & Wagenmakers, 2012) and science more generally (e.g., Baker, 2016). However, as we will show, the problem is likely worse (or at least more complicated) than they initially indicated. Specifically, for multivariate binary data, the marginal distributions enforce a large degree of structure on the data. We show that some expected measurements—such as commonly used centrality statistics—can have substantially higher values than what would usually be expected. As such, we propose a nonparametric approach to generate confidence intervals through Monte Carlo simulation. We apply the proposed methodology to the National Comorbidity Survey – Replication, provided by Forbes et al., finding that the many of the results are indistinguishable from what would be expected by chance. Further, we discuss the problem of multiple testing and potential issues of applying methods developed for 1-mode networks (e.g., ties within a single set of observations) to 2-mode networks (e.g., ties between 2 distinct sets of entities). When taken together, these issues indicate that the psychometric network models should be employed with extreme caution and interpreted guardedly. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Impact Statement:
- General Scientific Summary—We show that accounting for the base rates of criteria and controlling for the distribution of severity rates within a population can result in network models that are no different than random chance. As such, it is imperative that we validate these models with additional approaches and perspectives and not rely solely on psychometric network analytic approaches. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Comment/Reply
- Subjects:
- *Experimental Replication; *Psychopathology; *Symptoms; Causality; Inference
- PsycINFO Classification:
- Research Methods & Experimental Design (2260)
Psychological & Physical Disorders (3200) - Grant Sponsorship:
- Sponsor: National Institutes of Health, US
Grant Number: R01AA023248-01
Recipients: Steinley, Douglas; Sher, Kenneth J. - Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Jul 20, 2017; Revised: Jul 19, 2017; First Submitted: May 17, 2017
- Release Date:
- 20171106
- Correction Date:
- 20171127
- Copyright:
- American Psychological Association. 2017
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/abn0000308
- PMID:
- 29106283
- Accession Number:
- 2017-49368-014
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2017-49368-014&site=ehost-live">A method for making inferences in network analysis: Comment on Forbes, Wright, Markon, and Krueger (2017).</A>
- Database:
- PsycINFO
By: Douglas Steinley
Department of Psychological Sciences, University of Missouri;
Michaela Hoffman
Department of Psychological Sciences, University of Missouri
Michael J. Brusco
Department of Business Analytics, Information Systems & Supply Chain, Florida State University
Kenneth J. Sher
Department of Psychological Sciences, University of Missouri
Acknowledgement: Douglas Steinley and Kenneth J. Sher were partially supported by National Institutes of Health Grant R01AA023248-01.
We commend the authors—Forbes, Wright, Markon, and Krueger (2017)—for bringing to light one of the issues that has arisen recently when attempting to make inferences about the structure of so-called psychopathology networks. Specifically, Forbes et al. note that there are emerging regarding our ability to determine the extent that network structures obtained from two (or more) samples are similar or different in key respects. That is, do they replicate? Judging the similarity of various symptom network solutions can be challenging for a number of reasons, not the least of which is the number of parameters modeled including the number of edges (paths) connecting nodes (symptoms) and various measures that are derived from these, perhaps most critically, centrality measures that speak to the relative importance of individual symptoms vis a vis the total network. Depending upon the specific measures of centrality under consideration, such centrality measures could represent a powerful way to identify symptoms that are more fundamental (as opposed to accessory) to a given syndrome, bridge distinct symptom networks, or strongly related to other symptoms.
However, to have confidence in the results of any single network estimation procedure, it is important to be able to demonstrate its replicability, that is how similar are the estimated edge weights and centrality parameters across two different samples. Problems in establishing “reproducibility” is an increasing focus of concern in both psychology (e.g., Pashler & Wagenmakers, 2012) and science more generally (e.g., Baker, 2016). As noted above, the sheer number of parameters estimated make establishing the similarity of two more networks challenging and traditional approaches such as intuitive “eye balling” of the magnitude of various measures of agreement, concordance, or replicability (for instance, the Pearson correlation coefficient, the intraclass correlation)—many of which will not be appropriate—fails to provide a framework for making strong inferential statements regarding replicability of network parameterizations. Moreover, for technical reasons, measures of association based on binary data provide special challenges owing to their sensitivity to base rates (i.e., marginal frequencies) which may differ across samples either by design or sampling effects. Having a methodology for assessing network similarity that adequately addresses this challenge would make it possible for researchers to validly judge reproducibility and replicability.
In this commentary, we describe a simple yet effective method for generating empirical confidence intervals for whatever statistic is desired for inspection and demonstrate its use on a sample data set under the conditions described by Forbes et al. (2017). We then go on to provide some general comments regarding its application.
More specifically, we first review the notion of empirically creating appropriate null distributions for quantities of interest. We then discuss the application of such approaches to traditional one-mode networks where one set of nodes that are similar to each other or the same type (e.g., individuals). Following this, we provide an overview of the structure of two-mode matrices as commonly thought of in terms of network structure or graph theory, where there are two different types of nodes (e.g., symptoms and people). After that, we introduce the proposed hypothesis testing for two-mode binary networks and explicate the method by reanalyzing one of the data sets from Forbes et al. (2017) to obtain confidence intervals under an appropriate null distribution for edges and centrality statistics. This allows us to conduct subsequent statistical tests by determining whether the statistic of interest lies within the null confidence interval. If it does, then we conclude that the observed network statistic is performing the same as it would under a random two-mode network; if it does not, then a process of potential interest is likely driving the observed value for the tested statistic.
Background for Proposed TestIn multivariate statistics, it is often difficult to derive the exact sampling distribution for many quantities of interest. A standard practice is to generate random data with appropriate constraints, fit the hypothesized model to these generated data, and then compare the quantity of interest when computed under random data with that in the observed network. If they are very similar, then we cannot conclude that the observed data are any different than random data. Such an approach is often employed in other areas of multivariate statistics (e.g., use of parallel analysis in factor analysis, Horn, 1965).
In parallel analysis, the eigenvalues obtained from the covariance (or correlation) matrix derived from the sample data are compared with the eigenvalues obtained from covariance (or correlation) matrices arising from completely random data. The central idea behind of parallel analysis is that if a set of measured variables arises from a common factor model with f factors, then the f largest eigenvalues as computed from the data set should exceed that of the expected (e.g., average) eigenvalues of random data. One critical factor in making this comparison is how the random data are generated. Specifically, the constraints that are placed on each of the data sets in the comparison population are critical. If the eigen-decomposition is on the correlation matrix, a common constraint is the generated data should have the same sample size (e.g., the same number of observations) and the same number of variables. If the eigen-decomposition is on the covariance matrix, an additional constraint for consideration would be to ensure that the randomly generated variables had the same variances.
Similarly, corresponding procedures for comparing observed fit statistics to fit statistics derived from random data has been implemented in the domain of cluster analysis. Specifically, Steinley (2006, 2007, 2008) and Steinley and Brusco (2011) generated distributions based on random data to test the quality of a cluster solution and to determine whether the correct number of clusters had been chosen, respectively. Using this general approach, Steinley (2004) developed a method for sampling cluster agreement-matrices to approximate the statistic’s sampling distribution. Although used extensively in multivariate statistics, such approaches have also seen use in traditional network analyses—as explained in the following section.
One-Mode Networks
For one-mode networks (e.g., the rows and the columns of the adjacency matrix describe the same entities, such as a friendship network) statistical inference has long been an active area of research and methodological development because the standard inferential techniques relying on independence of observations does not apply. As a solution, it has been argued that the testing network statistics of interest can be obtained by generating a null distribution from a population of networks that have an appropriate structure that the researcher wishes to control. Generally, these networks are generated uniformly (e.g., each network has an equal chance of being selected and included in the population. Anderson, Butts, and Carley (1999) described an approach to conduct hypothesis testing on network level statistics when controlling for the number of observations (e.g., the number of actors or nodes) and the overall density (e.g., the number of links). Additional constraints can be added to the generation process, such as controlling for the number of mutual, asymmetric, and null ties. Furthermore, for one-mode networks, the notion of generating networks with specific properties has flourished with recent developments in exponential random graph modeling (see Hunter & Handcock, 2006).
Two-Mode Matrices
Two-mode data binary data has long been of interest in the psychological sciences (Arabie & Hubert, 1990; Arabie, Hubert, & Schleutermann, 1990; Brusco & Steinley, 2006, 2007a, 2007b, 2009, 2011; Brusco, Shireman, & Steinley, in press; Rosenberg, Van Mechelen, & De Boeck, 1996). In terms of networks and graph theory, two-mode binary data can be thought of as a bipartite graph and represented in a standard data matrix with n rows and p columns (where, often, rows represent the subjects and columns represent the variables). In the network literature, these are often deemed affiliation matrices. Until recently, there has been a dearth of methods for generating binary affiliation matrices with fixed margins, with the ones being used somewhat cumbersome and inefficient and too slow for application to large networks (Admiraal & Handcock, 2008; Chen, Diaconis, Holmes, & Liu, 2005). However, within the last few years, a pair of papers have appeared (Harrison & Miller, 2013; Miller & Harrison, 2013) that provide a computationally efficient method for generating affiliation matrices with fixed margins.
Interestingly, the motivating example for this work arises out of ecology to test cohabitation of species in biogeographical data, where the data are often of the form Species × Habitat. As a bit of background, original work by Diamond (1975) made extensive claims about “species assembly rules” that aimed to explain the interaction between specific species and their chosen habitats—indeed, some of the relationships between species and habitats were quite strong. However, Connor and Simberloff (1979) argued that, if one controlled for the base rates of the different species and the different habitats, then many of these so-called “strong” associations could have arisen by random chance. According to Miller and Harrison (2013), this set off years of argument about the nature of null hypothesis testing and whether to control for the marginal distributions or not. Owing to the fact that the effect disappears when the marginal distributions are controlled for, the current guidelines recommend that the marginal distributions are fixed such that observed effects are not due to the base rates of the rows and columns alone.
If the two-mode matrix is viewed as a contingency table, then fixing both marginal distributions corresponds to Fisher’s exact test, with the following assumptions:
- Each observation is classified into one and only one category of the row variable and into one and only one category of the column variable.
- The N observations come from a random sample such that each observation has the same probability of being classified into the ith row and the jth column as any other observation.
- The null hypothesis is: The event of an observation being in a particular row is independent of that same observation being in a particular column.
This framework will serve to generate the random matrices for our test, as described below.
Proposed TestThe goal of this commentary is to present such a test based on generating a set(s) of matrices from prespecified marginal distributions. The desire is to provide a statistical context for evaluating the magnitude some measures of correspondence with respect to the expected parameter estimates generated from random processes with known base rates. Further, much like the ecology literature, we argue that in the psychopathology literature it is imperative to compare observed results to null distributions that have the same prevalences of the symptoms themselves and the same distributions of severity across the individuals. This is a necessary condition to begin making precise, generalizable statements about specific symptoms by guaranteeing that any observed/interpreted relationships between the variables is attributable to their co-occurrence within the same individuals are not an artifact imposed by the marginal distributions.
Holding the within-person marginal distributions constant is necessary because the goal of network analysis in some ways is much more lofty that that of the common cause models, such as factor analysis. In the latter, it is often assumed that all items loading onto the same factor should be (theoretically) exchangeable; however, in network analysis the goal is to discover the explicit connections between individual items—as such, the items themselves take on a much more pronounced role in the modeling process. Specifically, for factor models, knowing that a subject endorsed 3 of 5 items on a disorder is often enough; in fact, that is what much of the diagnostic literature is predicated on. Conversely, for network models, the specific 3 items are much more important because the goal is to make causal (or pseudocausal) connections between specific items. The bar is much higher. Likewise, holding the within-item prevalences constant is extremely important as well. Specifically, it can easily be shown that items that have a higher prevalence of occurring are more likely to have observed edges in the network model. Consequently, it is important to model random items with the same rates of occurrence to ensure that observed network edges are not merely a byproduct of items that have been endorsed more than other items. Without such a reference, researchers will remain in the dark about what their findings indicate with these newer network models. The following two subsections describe how the procedure works, with a more mathematical explanation provided in the Appendix.
Algorithm for Within Network Importance
The ingredients for testing so-called “within” network importance are fairly straightforward. Namely, all that is required is the original data matrix Xn×p with n rows and p columns as well as the estimated network statistic of interest, say θ̂. The goal is then to obtain the sampling distribution for θ under the null distribution that the location of the ones (i.e., the presence of the symptom, say) in the binary matrix X - marginal distributions for both the rows and columns.
Procedurally, the process is fairly simple. First, the user must choose the number of random data matrices to generate. After this is chosen, each random matrix is generated such that it has identical column totals (e.g., each item prevalence is the same in the random matrices as in the observed matrix) and row totals (e.g., the severity profiles of the observations in the random matrices are the same as in the observed matrix). However, in the random generation process, the elements in the random matrices are generated such that the rows and columns are independent of each other conditional on the row totals and column totals (e.g., the marginal distributions) being the same. After all of the random matrices are generated, the statistic of interest (e.g., edge weights, centrality parameters, etc.) is computed on each of the random matrices. Finally, the statistic for the observed data is compared with the reference distribution derived from the random matrices, allowing for the calculation of a percentile score. If this score is between the 2.5 percentile and the 97.5 percentile, we can say that the observed data are providing results that are consistent with what we would expect to see in random data. That is, the p values associated with the estimates do not exceed a nominal level of.05.
Algorithm for Between Importance
The first algorithm discusses determining whether the observed data deviates from the what would be observed via random chance and concerns network elements that are internal to one data set (hence, the moniker of “within”). However, it is also possible to compute the correspondence or stability of network statistics across different data sets, which we will term as “between.” To compute the correspondence between two networks, in additional data set, Yn2×p, is required (note that while the number of variables must be the same, the two data sets may have different numbers of observations). Network structures are then derived from both X and Y, and a measure of correspondence is computed. Then, pairs of random matrices are generated, with one corresponding to X and one corresponding to Y, and the respective marginal distributions are fixed to those of the observed matrices. The same measure of correspondence is computed for each pair for random matrices, allowing for the generation of sampling distribution under random chance to be obtained. The computation of percentile scores than proceeds in the same fashion as described for the within network importance.
Relationship to Bootstrapping
Recently, Epskamp, Borsboom, and Fried (in press) have introduced methods for applying bootstrap techniques to psychological networks. These methods are designed to (a) assess the accuracy of estimated network connections, (b) investigate the stability of centrality indices, and (c) test whether network connections and centrality estimates for different variables differ from each other. The primary difference between the methodology proposed above and bootstrapping approach is that the former is assessing whether the observed effects are different than what we would expect from random chance while the latter assess stability and accuracy of coefficient estimation.
As such, it is possible, and as we see in the examples below, perhaps expected (because of the constraints placed on the network space due to the marginal distributions) that estimated network effects can be both simultaneously stable and not different than random chance. Additionally, a confidence interval around the either the connections between variables or the centrality estimates, as derived from the bootstrap approach, cannot include zero and still overlap with (or even fall within) the confidence interval indicating what we would expect by chance alone. In that instance, the estimate would be stable, but not particularly interesting/informative—and, in such a scenario we would caution against interpreting the estimate because, while the effect is shown to exist, it is not different than what would be expected by chance alone. We summarize the four possible outcomes derived from the outcomes of the two testing procedures in Table 1.
Four Potential Outcomes Between Bootstrap Procedure and Random Chance
In Table 1, we provide the more general possibility of testing hypotheses (or constructing confidence interval) concerned with comparing the effects with a prespecified null value, v0. Likewise, we can construct, from the procedure outlined above, the expected value of the effect under random chance, vrc. Then, the combined information of whether the estimate (v̂) is different than the hypothesized value (v0) and/or the value under random chance (vrc) is useful for determining the interestingness/importance of an effect. Obviously, the most interesting effect will be situations where it is shown that the estimate is both different from the hypothesized value and what is expected under random chance. Conversely, if effects are not different from what is expected under random chance, the information derived from the bootstrap method is uninteresting regardless of the degree of stability, accuracy, or difference from the hypothesized value. Lastly, when the effects are different from chance but not different from the hypothesized value, the result could be potentially interesting. In terms of the network diagram, this would correspond to unobserved links between pairs of variables. As such, each observed link can be interesting (different from zero, different from random chance) or uninteresting (not different from random chance); likewise, each unobserved link could be uninteresting (not different from zero, not different from random chance) or potentially interesting (not different from zero, different from random chance). We use “potentially” interesting because relationships (e.g., presence of links) and conditional independence (e.g., absence of links) are both inferred when the network diagram is visually inspected.
ExampleThe goal of these analyses is to infer the true underlying causal relationships between psychological symptoms, thus a significant result would be one that informs us of relations above and beyond what their prevalence rates tell us. Given the length constraints of the commentary, we focus on demonstrating the within-network evaluation algorithm for the NCS-R data as fit with the Ising model. Specifically, we will demonstrate the construction of confidence intervals around the estimated edge weights and centrality statistics as estimated using the qgraph package in the R statistical computing environment. To begin with, we need the marginal distributions of the NCS-R data. The frequency of each symptom is provided in Table 2, while the severity frequency (e.g., the number of symptoms endorsed by each subject, ranging from 0–18) is provided in Figure 1. From these data, we can immediately see the issue with only fixing one of the distributions. For instance, if we only fixed the prevalence of each symptom, then they would be distributed equally across the subjects, resulting in the average subject being assigned 3.75 symptoms. This would completely ignore the role of the severity continuum and its distribution in forming the network structure. Likewise, if we fixed only the severity marginals, we would see equally distributed counts over all of the symptoms, resulting in each having the same expected prevalence; consequently, differing prevalence rates alone could potentially define the “network” structure.
Frequency of Symptoms in the NCS-R Data
Figure 1. Frequency distribution of number of symptoms in NCS-R data showing the number of individuals with different numbers of symptoms.
Centrality Statistics
We implemented the algorithm as described above. For this small example, we generated 1000 matrices with marginals fixed to the values indicated in Table 2 and Figure 1. For each parameter of interest, we sorted the 1000 values from highest to lowest and created an empirical 99% confidence interval by taking the 5th and 995th ordered value. While we provide 99% confidence intervals here, more discussion is provided at the end of this section regarding choosing appropriate confidence levels. The confidence intervals for the centrality statistics are displayed in Table 3. From Table 3, we see that there are some variables that have significant centrality statistics when compared with what would be expected from chance. Significant values of centrality statistics are noted with an asterisk. For betweenness, we see that not all nonzero centrality statistics are significant, the most egregious being the betweenness score for “Anxiety about > 1 Event” is 38; however, we find that is not significantly different than would be expected by chance. This indicates that the magnitude of the centrality statistics does not necessarily indicate its relevance. We see similar results for closeness centrality, where 33% of the statistics are significant; unfortunately, without a formal test, it is impossible to determine which are different than chance as all of the estimates are within.017 of each other and magnitude alone does not confer significance. For strength, about two thirds of the centrality statistics are significant.
Confidence Intervals for Centrality Statistics
Finally, it is important to note that sometimes the centrality statistics are significantly less than would be expected by chance. This is true for “Chronic Anxiety” for betweenness and closeness, as well as “Muscle Tensions” for strength. This complicates the evaluation of this type of output because it is unknown whether observed results are significantly greater than chance, significantly less than chance, or no different than chance. Once again, the necessity for a formal test is highlighted.
Edge Weights
The difficulty is exacerbated for evaluating the edgeweights as displayed in Table 4. In Table 4, a “+” in the lower-triangle indicates the estimated edgeweight is significantly greater than chance, a “−” indicates the estimated edgeweight is less than chance, and a blank indicates that the value fell within the 99% confidence interval. One of the first things to notice is the prevalence of minus signs in Table 4. This indicates the absence of a link is just as important as the presence of a link. Of the 153 possible links, only 99 were significantly different than chance (65%), indicating that approximately one third of the estimated network is functioning equivalently to what we would expect by chance. Furthermore, with the three possible classifications of any individual edgeweight being (a) significantly greater than expected by chance, (b) significantly less than expected by chance, and (c) no different than expected by chance, the edgeweights are classified into those categories at the almost equal rates of 30%, 35%, and 35%, respectively. This uniform distribution of edgeweights to the three possible outcomes indicates that evaluating the results from these models by mere inspection of the edgeweights themselves is noninformative.
Estimates and Significance for Edge Weights
Once again, however, the specific edgeweights that are significant cannot be deduced by any pattern of edgeweights or their raw magnitude. For instance, the probability of an edgeweight being no different than chance if the observed edgeweight is greater than zero is 44%—so an observed edgeweight greater than zero is almost as equally as likely to be due to chance as not. To make it more salient, that would mean that nearly half of the edges depicted on the left side of Figure 3 in the Forbes et al. article are due to chance alone. Additionally, some of the zeros are included in the confidence intervals and some are not. Specifically, the probability of an edgeweight being significantly worse than chance given the observed edgeweight is less than or equal to zero is 28%. In terms of the graphical representation, that would mean that nearly 3 of 10 of the lines that are absent on the left hand side of Figure 3 are absent because of chance. Such variability in the confidence of the fidelity of observed edges in the figure renders these types of depictions of networks almost useless. This calls for an implementation of a formal testing procedure to assess the relevance of reported edgeweights or their absence.
Figure 2 illustrates the four possible outcomes as described in Table 1. The upper-left panel indicates an “interesting” finding between depressed mood and loss of interest. The edgeweight (1.93) falls above the distribution for random chance, and the bootstrap confidence interval around the edgeweight does not include zero. The lower-left panel indicates a “potentially interesting” connection between sleep problems (depression) and anxiety about more than one event. Specifically, the estimated value of zero is nonsignificant by both the eLasso and the bootstrap confidence interval around the estimate (0.00); however, the distribution based on random chance alone would expect this connection to be greater than what was observed. The final two panels on the right indicate two different “uninteresting” scenarios. The lower right panel shows the distribution for the edgeweight between chronic anxiety and sleep problems from anxiety—both distributions encompass the observed estimate of zero. The upper-right panel indicates a situation, in this case the relationship between loss of interest and psychomotor disturbances. The bootstrap test indicates that the edgeweight is significantly different than zero; however, the distribution derived from random matrices indicates that the observed value of 0.52 is not unexpected.
Figure 2. Examples of bootstrap compared with random distribution.
Confidence Levels
Above, we used a 99% confidence level to help control for multiple tests and illustrate the general notion of the differences between generating confidence intervals that reflect accuracy and stability through a bootstrap procedure versus the types of confidence intervals that aid in determining whether effects are different than would be expected by random chance alone. However, it is imperative to realize that even a more stringent confidence interval level will not aid in overcoming the threat of Type I error rates in the context of so many tests. Generally, there are
different tests for edgeweights and 3p tests for centrality measures (e.g., one for each variable on strength, closeness, and betweenness). In Table 4, there are
unique effects—for an α = .05, there would be (.05)153 = 7.65 significant effects based on chance alone, with the more stringent α = .01, (.01)(153) = 1.53 significant effects are expected to be attributable to chance alone.
In situations that rely on multiple testing, it is common to correct each individual test’s alpha to obtain an overall familywise α that corresponds to the prespecified value. The most conservative adjustment is the Bonferroni correction, which divides the familywise α by the total number of tests to obtain an appropriate level of α for each individual test—in this case, the adjusted α is (.05)/153 = .0003. As Epskamp, Borsboom, and Fried (in press) indicate, this type of correction can be overly burdensome and lead to the possibility of not finding any of the true effects (e.g., Type II errors). Beyond the inherent inverse relationship between Type I and Type II error, the computational burden to generate the requisite number of bootstrap samples for such small values of α can result in the procedure not being used in practice. For instance, generating enough samples to be able to obtain a 99.97% confidence interval would require around 10,000 samples; however, somewhere in the neighborhood of 50,000–10,000 would be preferable. As such, in their recommendations, Epskamp et al. (in press) indicate that corrections for chance will be relegated to future research, and in the interim, it is appropriate to proceed with uncorrected tests for the edgeweights.
While the sentiment to proceed with testing each test at α = .05 (the default setting in the Espkamp et al. bootstrapping software) is understandable, cautions, nonetheless, are called for. First, the probability of committing at least one Type I error when conducting multiple tests is 1 - (1 - α)T, where T is the number of tests being conducted. For instance, if you conduct two tests and do not correct for α the probability of committing at least one Type I error is 1 − (.95)2 = .0975; however, for the example above, the probability of making at least one Type I error is 1 − (.95)153 = .9997—an almost certainty. Furthermore, it is well known that the magnitude of p values cannot be compared to determine which effects are more likely to be significant and which are more likely to be either insignificant or a potentially a Type I error. The inability to do such a ranking is readily seen if one considers alternative correction methods beyond the Bonferroni correction. Specifically, the Holm-Bonferroni method (Holm, 1979) is uniformly more powerful than the Bonferroni correction; however, it proceeds by comparing the lowest p value to the strictest criterion, and then, as p values increase, the comparison criteria becomes less stringent. A similar test that is more powerful, but require assumptions regarding the joint distribution of the test statistics is Hochberg’s step-up procedure (Hochberg, 1988); likewise, one could consider Hommel’s (1988) stagewise rejective multiple test procedure. Unfortunately, each of these procedures would require in increase in the number of bootstrap samples—which would lead to the potential relaxation of the requirement to control for the familywise error rate.
Beyond the potential to incorrectly assume that effects are significant when they are not, the inattention to appropriately control for multiple testing has the potential to call into question all prior research in an area. This can be seen best with the application of multiple testing in fMRI research, another area that relies on a large number of tests and constructs substantive theory from patterns of significance. Recently, Eklund, Nichols, and Knutsson (2016) demonstrated that somewhere in the neighborhood of 40,000 fMRI studies may be invalid due to the inability to control for Type I error rates appropriately. Furthermore, in their conclusion the authors make a particularly relevant statement: “Although we note that metaanalysis can play an important role in teasing apart false-positive findings from consistent results, that does not mitigate the need for accurate inferential tools that give valid results for each and every study.” Because we are in the early stages of developing psychological networks, the most prudent course of action is also establishing appropriate inferential tools immediately and prevent the need to potentially correct hundreds or even thousands of published studies 5, 10, or 20 years from now. This view of tightening the reins for publication is further supported by the recommendation of a group of statisticians who recently weighed in on the reproducibility of psychological science (Johnson et al., 2017): “The results of this reanalysis provide a compelling argument for both increasing the threshold required for declaring scientific discoveries and for adopting statistical summaries of evidence that account for the high proportion of tested hypotheses that are false.” In fact, they suspect that potentially 90% (!!!) of tests performed in psychology experiments are testing negligible effects.
Fidelity of Data
Lastly, we note that there are several decisions made at the data analytic level that can affect and alter the results of any given network model. Some examples would include how the data are collected or how the responses are coded. For instance, it is possible that specific criteria could be coded differently depending on the interview employed to collect the data. Another example would be the so-called “skip out” issue that can occur in epidemiological questionnaires. For “skip out” items, there are gateway items that are assessed first—if the response to the gateway item is negative, then the remainder of the items are not asked, whereas if the response to the gateway item is affirmative, then all subsequent related items are asked. A question arises on how to handle the subsequent items if they were not assessed. A common approach is to assume that if the gateway item is not endorsed, then all subsequent subitems are not endorsed either—this is akin to a logical data imputation. Whether this is appropriate or not will depend on the specific set of questions and criteria being assessed. If it is inappropriate, the estimated effects will be biased; however, that being said, these biases will not effect the validity of the testing approach described above, nor does it mitigate the issues with not controlling for multiple testing.
Discussion Replicability
From these findings, we see that the results of many network analyses, as conducted on binary data, might be overstating their findings. Although space limitations only allowed for a modest analysis of the performance the Isingfit model on one data set, current work is expanding the investigation to a broader class of binary matrices as well as extending to the three other types of network models mentioned by Forbes et al. (2017). Preliminary results indicate that the findings will be similar to what was observed for the Isingfit model. In short, we second the conclusion of Forbes et al. and offer the addendum that discovering believable results, at the specific symptom level (whether that is the relationship between pairs of symptoms) or variable level statistics (e.g., centrality statistics), will likely be much more difficult than previously envisioned. As shown above, this is because empirical findings are difficult to distinguish from random chance, and we do not believe that it would be too strong of a suggestion that previously published findings using this methodology should be reevaluated using the above testing procedure. Without this additional testing, future research based on existing findings will likely lead to a significant degree of nonreplicability as the findings are potentially no difference than chance. While the network approach represents an important alternative view of diagnostic systems that could provide new insights into both the basic structure of psychopathology and identify promising targets for intervention by virtue of their centrality, existing methods must be considered unproven. Research practitioners must appreciate the limitations of the existing state-of-the-art and develop and refine approaches likely to provide more robust and interpretable solutions.
Are Psychopathology “Networks” Actually Networks?
In the first footnote, we note that much of the terminology in recent psychopathology network analysis has been borrowed from the traditional network analysis literature—much of which is rooted in psychology and sociology (see Wasserman & Faust, 1994). To determine whether the transferability of methods in traditional network analysis to psychopathology networks is warranted (or should be taken at face value), it is worth highlighting the differences between the two. Generally, there are two types of networks that can be considered: (1) networks that directly assess the relationships between the same set of observations (e.g., one-mode matrices as described above), and (2) affiliation networks where the connections are assessed between two sets of observations (two-mode matrices as described above). Clearly, psychopathology networks fall into the class of affiliation matrices where the connections are measured between observation and diagnostic criteria. The relationships between the criteria are then then derived by transforming the two-mode affiliation matrix to a one-mode so-called “overlap/similarity” matrix between the criteria, where traditional network methods are applied to this overlap/similarity matrix. Faust (1997) indicated that potential pitfalls arise when applying standard centrality measures to networks derived from affiliation matrices: “In going from the affiliation relation to either the actor co-membership relation or the event overlap relation, one loses information about the patterns of affiliation between actors and events. Thus, one needs to be cautious when interpreting centralities for these one-mode relations” (p. 189). The concerns of Faust (1997) are related directly to the concerns raised in the introduction where the methods developed for one-mode networks are applied to the two-mode networks (e.g., affiliation network or bipartite graph).
ConclusionAs Johnson et al. (2017) argue, the editorial policies (and funding priorities) must be compelled to adapt to higher standards prior to putting their stamp of approval on results. Given the nature of problems that confront psychometric network modeling, including (but not limited to): (a) the possibility of observing “significant” effects that are not different than random chance, (b) the difficulties induced by conducting numerous significant tests, which is not controlled for via bootstrapping, and (c) the uncertainty regarding applying traditional methodology developed for one-mode networks to two-mode networks, we wholeheartedly agree with the recommendation by Forbes and company to turn a skeptical eye toward these models. Additionally, given the results from the example above and the theoretical issues that surround multiple testing and appropriate reference distributions, we do not agree with Epskamp, Borsboom, and Fried (in press) that it is reasonable to continue conducting tests at the α = .05 while we wait for methodologists to develop procedures that address the shortcomings. Rather, it is the other way around: wait until methodologists develop the appropriate fixes then proceed with fitting these models. Not following such an approach runs the very real risk of creating a series of publications that contain results that are not reproducible and likely no different than what is expected under one of the most basic models of chance in all of categorical data analysis.
Although we have provided numerous caveats and cautions to employing psychometric network models, we do believe that they are opening a potentially important area of research for us to consider. Specifically, the moving more to a causal structure model, as opposed to a common cause (such as latent variable) model holds great appeal as the parallelism to traditional medical models of diseases is enhanced. In our view, it is likely that the true model (or at least the best fitting models) will be a hybrid of network models and latent variable models. Further, although we provide extreme caution in using these methods to motivate theory, we do believe that such techniques can be a useful tool in the arsenal of the researcher when used to supplement a rich, substantive knowledge regarding the psychopathology being studied. Additionally, we note that by using correlations (or functions thereof) the network models have wed themselves closely to traditional latent variable models in terms of how the relationships between variables are conceptualized. However, there is a rich history in assessing the similarity between binary items in general and elements within a bipartite graph specifically. Broadening approaches beyond the logistic regression framework encapsulated by the Isingfit approach (and perhaps even abandoning the notion of correlation as the foundational measure of association) could open the entire field of psychometric networks to entirely new horizons. Finally, many of the concerns raised in this commentary could be alleviated if we move from the current state of exploratory psychometric networks to confirmatory psychometric networks, allowing a priori specified effects to be tested rather being hamstrung out of the gate by the need to correct for multiple testing.
Footnotes 1 We note that there is a good deal of appropriation of terminology from the social network analysis literature used in these analyses; we would caution too much transference as the psychopathology networks are induced from bipartite graphs versus being observed directly.
2 The eigenvalues from the completely random data are obtained by averaging over several—usually one thousand or more—sets of eigenvalues that have been obtained from independently generated random data.
3 Such an approach has been superceded by the derivation of a closed-form solution for comparing the equivalence of two partitions (Steinley, Brusco, & Hubert, 2016), but the basic logic still applies.
4 We note that most of the time v0 = 0.
5 Note that under many situations, the expected value of the effect is often zero (e.g., no effect). For instance, in the simple case of a t test or a linear regression model, generating data from random distributions—accomplished by randomly assigning group labels for the t test or randomly generating predictors uncorrelated with the dependent variable for regression—we would expect mean differences of zero and slopes of zero. The deviation from no effect for these psychological networks is due to the restriction of the range of the sample space of potential networks that is induced from the observed marginal distributions.
6 The between network importance algorithm would be implemented in a similar fashion, but because of space constraints we have not included an example here.
7 The data set was provided to us by the first author, Miriam Forbes, of the target article.
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APPENDIX APPENDIX A: Algorithms for Testing Replicability when Marginal Distributions are Fixe
The ingredients for testing so-called “within” replicability are fairly straightforward. Namely, all that is required is the original data matrix Xn×p with n rows and p columns as well as the network statistic of interest, say θ̂. The goal is then to obtain the sampling distribution for θ under the null distribution that the location of the ones in the binary matrix X are random subject to the observed marginal distributions for both the rows and columns.
The algorithm proceeds as follows:
- Compute θ̂ from X; compute rx and cx (the marginal distributions for the rows and columns, respectively, which in this case are just sums across the columns and rows).
- Choose the number of random data matrices, R, to generate.
- Generate the ith random matrix, Mi, such that rmi = rx and cMi = cx (e.g., the marginal distributions of the random matrices will be equivalent to those of the observed matrix, X).
- Compute θi for i = 1, . . ., R. Order the θ’s from smallest to largest, letting the ordered vector of θ be denoted as θ(o).
- Create a confidence interval under the null distribution of random association as
. - If the observed test statistic, θ̂, falls within the interval in Step 5 then we cannot reject the null. Consequently, we conclude the observed value could have arisen by random chance alone.
Algorithm for Between Replicability
The algorithm for testing so-called “between” replicability is almost identical. All that is required is a second data matrix, Yn2×p, with which we want to compare the original data matrix xn1×p. The process proceeds as follows:
- Compute θ̂XY from f(X, Y), where f(·) is a function of correspondence between the two structures uncovered from X and Y (for instance, the function could be the correlation of the edge weights between the two network structures). Additionally, compute rx and cx from X; compute from Y, rY and cY from Y.
- Choose the number of pairs of random data matrices, R, to generate.
- Generate the ith pair of random matrices (Mi, Ni), such that rMi = rx and cMi = cx and rNi = rY and cNi = cY.
- Compute θi for i = 1, . . ., R. Order the θ’s from smallest to largest, letting the ordered vector of θ be denoted as θ(o).
- Create a confidence interval under the null distribution of random association as
. - If the observed test statistic, θ̂, falls within the interval in Step 5 then we cannot reject the null. Consequently, we conclude the observed value could have arisen by random chance alone.
Submitted: May 17, 2017 Revised: July 19, 2017 Accepted: July 20, 2017
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Source: Journal of Abnormal Psychology. Vol. 126. (7), Oct, 2017 pp. 1000-1010)
Accession Number: 2017-49368-014
Digital Object Identifier: 10.1037/abn0000308
Record: 5- Title:
- A randomized clinical trial of Motivational Interviewing to reduce alcohol and drug use among patients with depression.
- Authors:
- Satre, Derek D.. Department of Psychiatry, University of California, CA, US, dereks@lppi.ucsf.edu
Leibowitz, Amy. Division of Research, Kaiser Permanente Northern California Region, CA, US
Sterling, Stacy A.. Division of Research, Kaiser Permanente Northern California Region, CA, US
Lu, Yun. Division of Research, Kaiser Permanente Northern California Region, CA, US
Travis, Adam. Department of Psychiatry, Kaiser Permanente Southern Alameda, CA, US
Weisner, Constance. Department of Psychiatry, University of California, CA, US - Address:
- Satre, Derek D., University of California, San Francisco, 401 Parnassus Avenue, Box 0984, San Francisco, CA, US, 94143, dereks@lppi.ucsf.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 84(7), Jul, 2016. pp. 571-579.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 9
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- depression, alcohol, cannabis, hazardous drinking, motivational interviewing
- Abstract (English):
- Objective: This study examined the efficacy of Motivational Interviewing (MI) to reduce hazardous drinking and drug use among adults in treatment for depression. Method: Randomized controlled trial based in a large outpatient psychiatry program in an integrated health care system in Northern California. The sample consisted of 307 participants ages 18 and over who reported hazardous drinking, drug use (primarily cannabis) or misuse of prescription drugs in the prior 30 days, and who scored ≥5 on the Patient Health Questionnaire (PHQ-9). Participants were randomized to receive either 3 sessions of MI (1 in person and 2 by phone) or printed literature about alcohol and drug use risks (control), as an adjunct to usual outpatient depression care. Measures included alcohol and drug use in the prior 30 days and PHQ-9 depression symptoms. Participants completed baseline in-person interviews and telephone follow-up interviews at 3 and 6 months (96 and 98% of the baseline sample, respectively). Electronic health records were used to measure usual care. Results: At 6 months, MI was more effective than control in reducing rate of cannabis use (p = .037); and hazardous drinking (≥4 drinks in a day for women, ≥5 drinks in a day for men; p = .060). In logistic regression, assignment to MI predicted lower cannabis use at 6 months (p = .016) after controlling for covariates. Depression improved in both conditions. Conclusions: MI can be an effective intervention for cannabis use and hazardous drinking among patients with depression. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Impact Statement:
- What is the public health significance of this article?—Hazardous drinking and drug use are common among patients with depression. Results of this study indicate that motivational interviewing is a promising treatment approach to assist these patients. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Alcohol Abuse; *Drug Abuse; *Drug Rehabilitation; *Major Depression; *Motivational Interviewing; Cannabis; Prescription Drugs
- PsycINFO Classification:
- Drug & Alcohol Rehabilitation (3383)
- Population:
- Human
Male
Female
Outpatient - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs)
Aged (65 yrs & older) - Tests & Measures:
- Alcohol Readiness Ruler
Cannabis Readiness Ruler
Addiction Severity Index DOI: 10.1037/t00025-000
Patient Health Questionnaire-9 DOI: 10.1037/t06165-000 - Grant Sponsorship:
- Sponsor: National Institutes of Health
Grant Number: R01AA020463; P50DA009253
Recipients: No recipient indicated - Methodology:
- Clinical Trial; Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Mar 17, 2016; Accepted: Feb 8, 2016; Revised: Jan 20, 2016; First Submitted: Jun 17, 2015
- Release Date:
- 20160317
- Correction Date:
- 20160623
- Copyright:
- American Psychological Association. 2016
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/ccp0000096
- PMID:
- 26985728
- Accession Number:
- 2016-13461-001
- Number of Citations in Source:
- 45
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2016-13461-001&site=ehost-live
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2016-13461-001&site=ehost-live">A randomized clinical trial of Motivational Interviewing to reduce alcohol and drug use among patients with depression.</A>
- Database:
- PsycINFO
Record: 6- Title:
- An empirical examination of distributional assumptions underlying the relationship between personality disorder symptoms and personality traits.
- Authors:
- Wright, Aidan G. C.. Western Psychiatric Institute and Clinic, University of Pittsburgh School of Medicine, PA, US, wright.aidan@gmail.com
Pincus, Aaron L.. Department of Psychology, Pennsylvania State University, PA, US
Lenzenweger, Mark F., mlenzen@binghamton.edu - Address:
- Lenzenweger, Mark F., Department of Psychology, State University of New York at Binghamton, Science IV, Binghamton, NY, US, 13902-6000, mlenzen@binghamton.edu
- Source:
- Journal of Abnormal Psychology, Vol 121(3), Aug, 2012. pp. 699-706.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 8
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- count regression, hurdle models, personality disorders, personality traits, zero-inflated distributions, symptoms
- Abstract:
- This article examines the relationship between personality disorder (PD) symptoms and personality traits using a variety of distributional assumptions. Prior work in this area relies almost exclusively on linear models that treat PD symptoms as normally distributed and continuous. However, these assumptions rarely hold, and thus the results of prior studies are potentially biased. Here we explore the effect of varying the distributions underlying regression models relating PD symptomatology to personality traits using the initial wave of the Longitudinal Study of Personality Disorders (N = 250; Lenzenweger, 1999), a university-based sample selected to include PD rates resembling epidemiological samples. PD symptoms were regressed on personality traits. The distributions underlying the dependent variable (i.e., PD symptoms) were variously modeled as normally distributed, as counts (Poisson, Negative-Binomial), and with two-part mixture distributions (zero-inflated, hurdle). We found that treating symptoms as normally distributed resulted in violations of model assumptions, that the negative-binomial and hurdle models were empirically equivalent, but that the coefficients achieving significance often differ depending on which part of the mixture distributions are being predicted (i.e., presence vs. severity of PD). Results have implications for how the relationship between normal and abnormal personality is understood. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Personality Disorders; *Personality Traits; *Statistical Regression; *Symptoms; Models
- Medical Subject Headings (MeSH):
- Adolescent; Female; Humans; Longitudinal Studies; Male; Models, Psychological; Personality; Personality Disorders; Personality Inventory; Self Report; Young Adult
- PsycINFO Classification:
- Personality Disorders (3217)
- Population:
- Human
Male
Female - Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- International Personality Disorder Examination
Revised Interpersonal Adjective Scales–-Big Five DOI: 10.1037/t10655-000 - Grant Sponsorship:
- Sponsor: National Institute of Mental Health, US
Grant Number: MH45448; F31MH087053
Recipients: Wright, Aidan G. C.; Lenzenweger, Mark F. - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Jun 25, 2012; Accepted: May 22, 2012; Revised: Apr 27, 2012; First Submitted: Oct 2, 2011
- Release Date:
- 20120625
- Correction Date:
- 20130114
- Copyright:
- American Psychological Association. 2012
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0029042
- PMID:
- 22732004
- Accession Number:
- 2012-16776-001
- Number of Citations in Source:
- 30
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-16776-001&site=ehost-live
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-16776-001&site=ehost-live">An empirical examination of distributional assumptions underlying the relationship between personality disorder symptoms and personality traits.</A>
- Database:
- PsycINFO
An Empirical Examination of Distributional Assumptions Underlying the Relationship Between Personality Disorder Symptoms and Personality Traits
By: Aidan G. C. Wright
Western Psychiatric Institute and Clinic, University of Pittsburgh School of Medicine;
Department of Psychology, The Pennsylvania State University;
Aaron L. Pincus
Department of Psychology, The Pennsylvania State University
Mark F. Lenzenweger
Department of Psychology, State University of New York at Binghamton;
Personality Disorders Institute, Weill Cornell Medical College;
Acknowledgement: This research was supported by grants (MH45448, Lenzenweger; F31MH087053, Wright) from the National Institute of Mental Health, Washington, DC. The views and opinions contained within are solely those of the authors and do not reflect the official position of the funding source.
This manuscript reports on work that represents a portion of the first author's dissertation. We thank Jerry S. Wiggins for providing consultation on the initial use of the Revised Interpersonal Adjectives Scales–Big Five, and Lauren Korfine for project coordination in the early phase of the study. We are indebted to Michael N. Hallquist for generously sharing his time to instruct the first author on the intricacies of the R statistical package. Emily B. Ansell, Nicholas R. Eaton, Christopher J. Hopwood, and Kristian E. Markon each provided helpful comments on an early draft of this work.
Personality disorder (PD) researchers have called for an integration of normal and pathological personality functioning within comprehensive dimensional models of personality (e.g., Depue & Lenzenweger, 2005; Widiger, Livesley, & Clark, 2009). It has been argued that normal personality exists on a continuum of functioning with PDs (Widiger & Trull, 2007). A large empirical literature examining the relationship between traits and PDs contributed to the seminal decision to use a dimensional trait system to conceptualize phenotypic variation in PD in DSM-5 (Skodol et al., 2011). This research relies primarily on cross-sectional correlations and linear regression to model the relationship between personality traits and PD symptoms. However, these analytic tools suffer from limitations when the underlying distribution of the variables is severely non-normal, as is the case with PD symptoms in the population. Alternative approaches that more accurately model the observed PD symptom distribution may provide better estimates of the relationship between personality and its disorder, and may offer new insights into the nature of that relationship.
The extant nosology of PDs represents personality pathology as a collection of “distinct clinical syndromes” (p. 689; American Psychiatric Association, 2000) that differ categorically from normative functioning and each other. These distinctions have been criticized as arbitrary and lacking in robust scientific support (Widiger & Trull, 2007), and diagnostic criteria treated as dimensional markers for disorders perform better by empirical standards (Morey et al., 2007). However, measuring disorders dimensionally cannot confirm their continuity with normal functioning (Lenzenweger & Clarkin, 2005), and the accumulated research presents a mixed picture related to this issue. Meta-analyses (Samuel & Widiger, 2008; Saulsman & Page, 2004) demonstrate that basic personality traits exhibit significant and replicable relationships to PD, but the association between traits and PDs are generally only modest in size (Mdn |r| = .15; range = .02–.54; Samuel & Widiger, 2008). Furthermore, in regression models, the five-factor domains and facets generally only explain a minority of the variance in PDs (e.g., Bagby, Costa, Widiger, Ryder, & Marshall, 2005). Thus, normal personality traits and PD are not interchangeable representations of functioning, despite clearly recognizable shared content (Krueger et al., 2011). Other researchers and theorists have highlighted the general impairment associated with PDs (Kernberg, 1984; Livesley & Jang, 2005; Pincus, 2005). Relatedly, a number of investigators have found that PD is primarily characterized by higher neuroticism, lower conscientiousness, and lower agreeableness, with less in the way of distinction beyond this core trait profile (Hopwood et al., 2011; Morey et al., 2002; Saulsman & Page, 2004). Thus, it may be that a particular combination of traits reflects personality pathology generally, with further differentiation occurring in the presence of this profile.
The question of continuity in personality and its pathology has taken center stage in the development of the DSM-5, which will shift to a dimensional model based on the robust empirical findings suggesting that PD is fundamentally dimensional in nature (Skodol et al., 2011). However, in part because of issues raised here, DSM-5 will distinguish between personality dysfunction and the description of that dysfunction using pathological traits (Hopwood et al., 2011; Krueger et al., 2011). Indeed, the sum of the empirical literature leaves an unclear picture of how PD and personality traits are related to each other. It may be that PD is dimensionally continuous with basic personality traits (e.g., Depue & Lenzenweger, 2005). Alternatively, continuities and discontinuities may exist in these relationships, with some driven by the presence of PD and others, perhaps more subtle, driven by the severity of PD beyond its presence (see Lenzenweger & Clarkin, 2005). What is clear is that the theoretical goal of integrating normative personality traits and PD remains elusive.
The key theoretical questions of how personality and PD relate to one another are also inherently questions of methodology. Dimensional approaches can make varied distributional assumptions that may have relevance for advancing the understanding of the relationship between normal and abnormal personality. Research examining this question relies almost exclusively on standard correlation and linear regression. These approaches make several important assumptions (i.e., normality of residuals, homoscedasticity, linearity of relationship, independence), that, when violated, can bias estimation (Cohen, Cohen, West, & Aiken, 2003). Less serious are biased standard errors, which can produce incorrect significance tests for parameters. More serious violations occur when the actual effect of a relationship is misestimated. A major contributing source to the violation of these assumptions is the distribution of the variables being modeled.
In the population, the actual distribution of PD symptoms is highly positively skewed with a majority of individuals suffering from no symptoms. Figure 1 provides an example of such a distribution using the narcissistic personality disorder (NPD) features in the first wave of the Longitudinal Study of Personality Disorders (LSPD; Lenzenweger, 1999), the dataset used here. This histogram is characteristic of a count distribution. Modeling techniques for counts are primarily based on the Poisson and Negative-Binomial (NB) distributions and can be used for regression when appropriate (Cameron & Trivedi, 1998; Long, 1997). Of note is the large number of zeros in the distribution, which has important implications for modeling the relationship between the symptoms and other variables of interest. These zeros carry important information about who does and does not possess symptoms of PD. With large numbers of zeros in the data, two potential modifications to basic count models are recommended: zero-inflated and hurdle models (Atkins & Gallop, 2007; Cameron & Trivedi, 1998; Long, 1997). Zero-inflated models estimate a group of individuals based on the excess of zeros beyond a standard Poisson or NB model, which are treated as individuals who can only take on a zero value. Hurdle models make a binary distinction between those who have a zero value versus those who have a nonzero value. Despite this distinction in the treatment of zero-values, both models estimate separate regression coefficients for the zero versus nonzero (e.g., absence vs. presence of PD) and the count (e.g., severity of PD) portion of the models. These models are ideal for evaluating whether the traits that give rise to any symptoms of PDs are the same as those that predict the number of symptoms once present.
Figure 1. Observed Longitudinal Study of Personality Disorders (LSPD) narcissistic personality disorder features.
The current study explores the relationship of personality traits to PD symptoms using regression models capable of appropriately modeling the distribution of symptoms in the population. We use the LSPD sample, which is made up of participants recruited both with and without significant pathology, unlike samples selected based on shared diagnostic status or for high levels of pathology. As a result, the distributions of PD symptoms closely matches those found in epidemiological samples (Lenzenweger, 2008). The LSPD dataset is ideal for the types of investigations pursued here because it captures the boundary between those individuals whose personalities function well and those who evidence dysfunction.
Our first aim was to evaluate whether the assumptions of linear regression are violated when predicting PD symptoms and to compare Poisson, zero-inflated Poisson (ZIP), Poisson hurdle (PH), negative-binomial (NB), zero-inflated negative-binomial (ZINB), and negative-binomial hurdle (NBH) regression models that predict PD symptoms from personality trait scores. Our second and more substantive aim involves comparing the patterns of significant regression coefficients to determine the effect of varying distributional assumptions on the relationship between traits and PDs. Attention is also given to differences in patterns in the prediction of presence versus severity of PD features in the zero-inflated/hurdle models.
Method Participants
Detail concerning the participant selection procedure in the LSPD is given elsewhere (Lenzenweger, 1999). The 250 participants were drawn from a nonclinical university population, are balanced on gender (53% women), and the mean age at entry into the study was 18.88 years (SD = 0.51). Approximately half of the participants were selected based on putative positive PD status as assessed by a self-report PD screener. This ensured an adequate sampling of PD pathology in a nonclinical population. Based on diagnostic assessments conducted by experienced clinicians, 11% of the participants qualified for a PD diagnosis of some sort, and 45.2% met the criteria for an Axis I diagnosis (Lenzenweger, 1999). Rates of diagnosed PDs in the sample were as follows: paranoid = 1.2%, schizoid = 1.2%, schizotypal = 1.6%, antisocial = 0.8%, borderline = 1.6%, histrionic = 3.5%, narcissistic = 3.1%, obsessive–compulsive = 1.6%, passive-aggressive = 0.8%, avoidant = 1.2%, dependent = 0.8%, and not otherwise specified = 4.3%. It is important to note that these rates closely mirror the rates of PD found in large epidemiological samples (Lenzenweger, 2008).
Measures
At baseline, participants completed a diagnostic clinical interview and self-report personality measures. Only data from these initial assessments are used here.
The International Personality Disorder Examination (IPDE; Loranger, 1999) was used to assess PD symptomatology. The DSM–III–R criteria were assessed in this study because these were in effect at the time the LSPD was undertaken. The interrater reliability for IPDE assessments (based on intraclass correlation coefficients) was excellent, ranging between .84 and .92 for all PD dimensions used for this study. For each symptom, an individual may receive a score of 0 (absent or normal), 1 (exaggerated/accentuated), or 2 (criterion/pathological). These values are summed within each disorder to create a count of disorder related features.
The Revised Interpersonal Adjective Scales–Big Five (IASR-B5; Trapnell & Wiggins, 1990) contains 124 trait descriptive adjectives rated on a 0 to 8 scale that provides scores for the personality trait dimensions of Dominance, Affiliation, Conscientiousness, Neuroticism, and Openness. Coefficient alphas ranged from .82 to .96.
ResultsA series of regression models were estimated in R Package PSCL (Zeileis, Kleiber, & Jackman, 2008). Each PD's count and the Total PD count were regressed on each personality trait score separately. A model was estimated for each personality trait separately in keeping with past literature, and because the traits are orthogonal in theory, but in practice often exhibit relationships that attenuate regression coefficients when entered simultaneously in a model. Trait predictors were standardized. For each trait-PD pairing, a set of models was estimated with Normal, Poisson, ZIP, PH, NB, ZINB, and NBH distributions specified for the PD counts.
The linear regression models were evaluated by testing linearity, normality of the residuals, and homoscedasticity. A minority (22%) of the models violated the assumption of linearity. However, all model residuals exhibited significant skewness (M = 2.75; range = 1.83–4.41) and excess kurtosis (M = 10.50; range = 3.59–25.89). Visual analyses suggested that the assumption of homoscedasticity was untenable for all models. In addition, 36% of the models predicted negative symptom counts. Thus, the assumptions of linear regression models do not hold and a considerable proportion result in the prediction of impossible values, all of which emphasizes the need for count based models.
Poisson regression models make strict assumptions about the variance of the observations that are frequently violated in applied research (Coxe, West, & Aiken, 2009). The NB differs from the Poisson in that it estimates an additional parameter for the variance of counts, and therefore these two models are nested and can be compared using likelihood ratio tests (LTRs; Long, 1997). In each case, the NB model was a significant improvement over its Poisson counterpart. Models which account for the large stack of zeros in the data using either zero-inflation or a hurdle are non-nested relative to the basic count models and each other, and therefore require an alternative test. To compare non-nested models, we employed a Vuong (1989) non-nested LRT. The Vuong LRT compares two models under the null hypothesis “that the competing models are equally close to the true data generating process” (p. 307) such that a significant statistic favors one model over the other, and a nonsignificant statistic suggests that the models are equivalent. When models are quantitatively equivalent additional criteria are required to select between models. One common approach is to select the model with the fewest estimated parameters, placing a premium on parsimony. Yet there may be a conceptual/theoretical preference for considering more complex models under these conditions. As Atkins and Gallop (2007) argue, a histogram like that depicted in Figure 1 would appear to suggest two separate processes: There are those individuals without any personality pathology, and among those that have it, a range of severity. They go on to note that the two-step count models are well-suited for investigating “psychological models in which there are two processes and where the determinants of those processes differ” (p. 733). Our results of a comprehensive comparison of models suggest that with few exceptions, the NB, ZINB, and NBH models are equivalent. When a model was favored, it was a two-step model, and between those the hurdle models. In addition, a number of ZINB models were nonidentified. Therefore, we retain for consideration the NB and NBH models.
Table 1 reports the regression coefficients for the Normal, NB, and NBH and their significance. No formula exists for transforming the different results to the same effect size for direct comparison across models. Therefore, what is most informative is the valence and significance level of each coefficient. Odds/rate ratios of 1.0 indicate no effect, and those below 1.0 indicate a negative association between predictor and outcome. Given the number of coefficients we highlight notable findings, and refer readers to Table 1 for a more detail. Importantly, similar patterns emerge across all models. Specifically, radical differences, such as a valence change, do not occur. However, pattern differences in significant coefficients across steps of the NBH models suggest different processes associated with the presence versus severity of PD symptoms of different types. We briefly summarize these findings organized around the trait dimensions.
Summary of Coefficients From Models Regressing Personality Disorder Symptoms on Personality Traits
Summary of Coefficients From Models Regressing Personality Disorder Symptoms on Personality Traits
Dominance most often predicted PD presence (schizoid, histrionic, dependent, obsessive-compulsive) and less commonly PD severity (narcissistic) or both (schizotypal, avoidant). Affiliation, in contrast, tended to predict both presence and severity (schizoid, schizotypal, antisocial, borderline, narcissistic, and obsessive-compulsive) and otherwise just presence (paranoid, avoidant) when considering individual PDs. Conscientiousness only predicted PD presence (paranoid, borderline, narcissistic, dependent). Neuroticism was a strong predictor, most commonly of both presence and severity (paranoid, schizotypal, borderline, histrionic, dependent, obsessive-compulsive). Interesting deviations from this include the fact that neuroticism only predicted severity in antisocial, but not presence, and presence but not severity in narcissistic features. Openness only predicted presence of Narcissistic PD, a result that is not easily interpreted. Finally, the Total PD symptom model is considered separately as the hurdle step predicting presence reflects a more stringent step between those with any PD and those with none at all. Here only neuroticism is predictive of both PD presence and severity, although conscientiousness and affiliation also predict severity.
DiscussionThe current study addresses an implicit assumption and likely limitation in much of the prior work linking personality traits and PD—specifically, although PD is not a normally distributed phenomenon in the population, it has consistently been modeled as such. First, we demonstrated that the basic assumptions of linear regression are violated and frequently result in the prediction of impossible values (i.e., negative counts). Second, we found that NB and NBH models do a comparable job of fitting the count distributions of PD symptoms and they cannot be distinguished quantitatively. Nevertheless, despite the flexibility of the NB distribution to account for a large proportion of individuals with zero values, this feature of the data is suggestive of distinct processes that are worth examining via two-step count models (Atkins & Gallop, 2007; Long, 1997). With these models interesting differences emerge across the two steps. When predicting individual diagnostic constructs, results suggest that neuroticism and affiliation are predictive of both PD presence and severity, whereas dominance is more often, and conscientiousness is exclusively, predictive of the presence of PD symptoms.
These results have implications for understanding the relationship between normal-range personality traits and PD. Issues related to both dimensionality and continuity in personality and PD have emerged from the proposed revisions for DSM-5. The proposed two-step diagnostic process for DSM-5 defines PD using a continuum of self/interpersonal impairment, with separate maladaptive personality traits provided to characterize phenotypic variation in the expression of an individual's core personality pathology. The models employed here are consistent with this approach, distinguishing between aspects of personality related to the presence of PD, and those related to its expression once present. Contrasting the results of the models predicting the count of all PD symptoms with the other model is also informative. The Total PD hurdle models are special in that they serve to represent general personality pathology, and the first step differentiates between those who have absolutely no pathology from those with any degree of pathology. The Total PD models suggest that only neuroticism differentiates those with any pathology from those without, which is consistent with research that shows it is an important predictor of myriad public health outcomes, psychological and otherwise (Lahey, 2009). Neuroticism also predicts severity along with low affiliation and low conscientiousness. This pattern of associations with severity was the same as that found by Hopwood and colleagues (2011) in a clinical sample. Findings here point to the fact that other variables besides normative traits (with the exception for a propensity to experience negative emotions) are responsible for the presence any PD, although other traits can characterize the variability in phenotypic expression of distinct PDs and overall PD severity. The implication is that normative traits alone are not ideal to differentiate normal from abnormal. The DSM-5 proposal to draw on self and interpersonal processes to define general personality pathology with traits used to clarify specific forms of PD is consistent with these results, although future research should incorporate the distinct predictors in these models to formally test the proposal.
A complementary way of understanding these results is that basic traits exert themselves at different levels of pathology. Limited prior research suggests that the level of traits across the spectrum of PD is nonlinear (O'Connor, 2005), indicating that normative traits may be more or less informative for distinguishing individuals at different levels of PD dimensions. Figure 2 illustrates this, continuing with NPD as the exemplar. Trait scores for those individuals without symptoms occur across the range of values, indicating that knowing someone's trait level without knowing their pathology is often diagnostically uninformative. For example, there are individuals at all levels of Dominance, including high levels, without any NPD symptoms. Yet, once there is any narcissistic pathology, rising severity is associated with increases in dominance. The opposite is true with neuroticism. Those without narcissistic pathology are lower on average, but once pathology is present, neuroticism is unrelated to severity. These fine-grained relationships suggest that PDs are not reducible to sums of basic traits, but are more complex in their structure of associations.
Figure 2. Scatter plots of personality trait scores and narcissistic personality disorder (NPD) features.
Several caveats must be mentioned. First, our present sample was more homogenous in age, education, and social class than the U.S. population. Second, given that the participants were selected from a population of first-year university students, the sample may have been somewhat censored for individuals affected by very severe PDs. However, the results from the linear regression models are highly consistent with prior work (e.g., Samuel & Widiger, 2008), suggesting generalizability. Additionally, the distributions of all psychiatric disorders in the LSPD sample are consistent with the U.S. population distribution (Kessler, Chiu, Demler, & Walters, 2005; Lenzenweger, 2008). Third, although some may feel that running individual models for each trait does not provide the most accurate picture of these relationships, the overwhelming majority of prior research adopts this approach, and therefore we employed it to provide a clear comparison for readers. Fourth, we should highlight that the linear regression models are still quite robust, and exhibit patterns of associations consistent with the NB models. Thus, we are confident that prior studies have accurately identified general relationships between traits and PD. However, a necessary next step in the empirical study of PD is to move beyond this level of analysis to elucidate the exact structure of these relationships in addition to identifying etiologic and mechanistic processes (e.g., underlying neurobehavioral systems, see Depue & Lenzenweger, 2005).
In summary, we examined the effect of varying the distribution of PD symptoms in regression models with personality traits. To appropriately model them requires the use of count distributions, and two-step count models provide opportunities to examine discontinuities in these relationships. In the past, modeling the distributions implemented here might have been more challenging, but a number of user-friendly statistical packages now include these as standard features. We used R, but Mplus, Stata, SAS, and SPSS can employ some or all of these distributions. When these approaches are adopted, a more refined picture emerges suggesting that PD is not merely the tail end of a continuous distribution of normal traits, and the traits associated with the presence of PD are not always those associated with increasing severity. Although we do not argue that these results are definitive and recognize that they should be replicated, we suggest that these analytic approaches are more appropriate, have the potential to elucidate some of the issues associated with continuity and discontinuity in personality and its pathology, and inform ongoing efforts to refine the diagnosis of PD.
Footnotes 1 The distribution in Figure 1 is highly representative of each of the PDs in the LSPD.
2 Full results available upon request.
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Submitted: October 2, 2011 Revised: April 27, 2012 Accepted: May 22, 2012
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Source: Journal of Abnormal Psychology. Vol. 121. (3), Aug, 2012 pp. 699-706)
Accession Number: 2012-16776-001
Digital Object Identifier: 10.1037/a0029042
Record: 7- Title:
- Are body dissatisfaction, eating disturbance, and body mass index predictors of suicidal behavior in adolescents? A longitudinal study.
- Authors:
- Crow, Scott. Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, US, crowx002@umn.edu
Eisenberg, Marla E.. Division of General Pediatrics and Adolescent Health, University of Minnesota, Minneapolis, MN, US
Story, Mary. Division of General Pediatrics and Adolescent Health, University of Minnesota, Minneapolis, MN, US
Neumark-Sztainer, Dianne. Division of General Pediatrics and Adolescent Health, University of Minnesota, Minneapolis, MN, US - Address:
- Crow, Scott, Department of Psychiatry, University of Minnesota Medical School, F290 2450 Riverside Avenue, Minneapolis, MN, US, 55454-1495, crowx002@umn.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 76(5), Oct, 2008. pp. 887-892.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 6
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- suicide, obesity, body dissatisfaction, eating disorders, suicidal ideation, body mass index
- Abstract:
- Disordered eating, body dissatisfaction, and obesity have been associated cross sectionally with suicidal behavior in adolescents. To determine the extent to which these variables predicted suicidal ideation and attempts, the authors examined these relationships in a longitudinal design. The study population included 2,516 older adolescents and young adults who completed surveys for Project EAT-II (Time 2), a 5-year follow-up study of adolescents who had taken part in Project EAT (Time 1). Odds ratios for suicidal behaviors at Time 2 were estimated with multiple logistic regression. Predictor variables included Time 1 extreme and unhealthy weight control behaviors (EWCB and UWCB), body dissatisfaction, and body mass index percentile. Suicidal ideation was reported by 15.2% of young men and 21.6% of young women, and suicide attempts were reported by 3.5% of young men and 8.7% of young women. For young women, suicidal ideation at Time 2 was predicted by Time 1 EWCB. The odds ratio for suicide attempts was similarly elevated in young women who had reported EWCB at Time 1. These odds ratios for both suicidal ideation and suicide attempts remained elevated even after controlling for Time 2 depressive symptoms. In young men, EWCB was not associated with suicidal ideation or suicide attempts 5 years later. Body mass index and body dissatisfaction did not predict suicidal ideation or suicide attempts in young men or young women. These results emphasize the importance of EWCB. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Body Image; *Body Mass Index; *Eating Disorders; *Obesity; *Suicide; Dissatisfaction; Suicidal Ideation
- Medical Subject Headings (MeSH):
- Adolescent; Body Image; Body Mass Index; Cohort Studies; Depression; Feeding and Eating Disorders; Female; Humans; Longitudinal Studies; Male; Minnesota; Obesity; Personality Inventory; Psychometrics; Risk Factors; Sex Factors; Suicide, Attempted; Young Adult
- PsycINFO Classification:
- Psychological & Physical Disorders (3200)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Grant Sponsorship:
- Sponsor: US Department of Health and Human Services, Health Resources and Services Administration, Maternal and Child Health Bureau, US
Grant Number: R40 MC 00319
Recipients: No recipient indicated
Sponsor: Minnesota Obesity Center, US
Grant Number: P30DK 60456
Recipients: No recipient indicated
Sponsor: National Institute of Mental Health
Grant Number: K02 MH 65919
Recipients: No recipient indicated - Methodology:
- Empirical Study; Longitudinal Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Apr 25, 2008; Revised: Apr 14, 2008; First Submitted: Aug 8, 2007
- Release Date:
- 20081006
- Copyright:
- American Psychological Association. 2008
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0012783
- PMID:
- 18837605
- Accession Number:
- 2008-13625-017
- Number of Citations in Source:
- 38
- Persistent link to this record (Permalink):
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2008-13625-017&site=ehost-live">Are body dissatisfaction, eating disturbance, and body mass index predictors of suicidal behavior in adolescents? A longitudinal study.</A>
- Database:
- PsycINFO
Are Body Dissatisfaction, Eating Disturbance, and Body Mass Index Predictors of Suicidal Behavior in Adolescents? A Longitudinal Study
By: Scott Crow
Department of Psychiatry, University of Minnesota Medical School, Minneapolis;
Marla E. Eisenberg
Division of General Pediatrics and Adolescent Health, University of Minnesota, Minneapolis
Mary Story
Division of General Pediatrics and Adolescent Health, University of Minnesota, Minneapolis;
Division of Epidemiology and Community Health, University of Minnesota, Minneapolis
Dianne Neumark-Sztainer
Division of General Pediatrics and Adolescent Health, University of Minnesota, Minneapolis;
Division of Epidemiology and Community Health, University of Minnesota, Minneapolis
Acknowledgement: This study was supported by Maternal and Child Health Bureau Grant R40 MC 00319 from the Health Resources and Services Administration, Department of Health and Human Services; by Minnesota Obesity Center Grant P30DK 60456; and by National Institute of Mental Health Grant K02 MH 65919.
Suicide is a leading cause of death in adolescents (Goldsmith, 1986), and suicidal ideation and suicide attempts are common. In reviewing the published literature on suicidal behaviors during adolescence, Evans, Hawton, Rodham, and Deeks (2005) found mean lifetime rates of suicidal ideation of 29.9%; of respondents, 9.7% had reported suicide attempts. It appears that the frequency of suicidal behavior is typically underestimated by families (Breton, Tousignant, Bergeron, & Berthiaume, 2002; Klimes-Dougan, 1998) and might be underestimated by clinicians. Suicidal behavior results in substantial utilization of emergency care and hospitalization (Olfson, Gameroff, Marcus, Greenberg, & Shaffer, 2005).
Numerous correlates of suicidal behavior during adolescence have been identified. These correlates include family history of suicidal behavior (Brent & Mann, 2005), substance use disorders, early childhood maltreatment (Bridge, Goldstein, & Brent, 2006), and psychiatric disorders (Andrews & Lewinsohn, 1992; Brent et al., 1988; Fergusson, Beautrais, & Horwood, 2003; Kessler, Borges, & Walters, 1999). The psychiatric disorders implicated include, most prominently, mood disorders and eating disorders but also anxiety disorders and psychotic disorders. It appears that eating disorders may carry the highest suicide risk of any psychiatric disorder (Franko & Keel, 2006; Harris & Barraclough, 1994).
Although the link between diagnosable eating disorders and suicidal behavior has been recognized for some time, recent evidence suggests a correlation between suicidal behavior and body dissatisfaction, as well as disordered eating behaviors that fall short of the threshold for eating disorder, not otherwise specified, diagnosis (Ackard, Neumark-Sztainer, Story, & Perry, 2003; Crow, Eisenberg, Story, & Neumark-Sztainer, 2008; Rafiroiu, Sargent, Parra-Medina, Drane, & Valois, 2003). For example, in a school-based study of 13-year-olds, higher levels of body dissatisfaction predicted suicidal attempts over 2-year follow-up (Rodriguez-Cano, Beato-Fernandez, & Llario, 2006). Similarly, Crow et al. (2008) reported that unhealthy weight control behaviors (UWCB) and body dissatisfaction both correlated cross-sectionally with suicidal behavior in adolescents. In a sample of high-school-age adolescents, body image and, in particular, body attitudes and feelings predicted suicidal ideation (Brausch & Muehlenkamp, 2007).
Whether overweight and obesity are correlated with suicidal behavior has been unclear; some studies have found such a correlation (Carpenter, Hasin, Allison, & Faith, 2000; Falkner et al., 2001; Moore, Stunkard, & Srole, 1962), but others have not (Crow et al., 2008; Hallstrom & Noppa, 1981; Kittel, Rustin, Dramaix, deBacker, & Kornitzer, 1978; Neumark-Sztainer, Story, French, et al., 1997). Association of obesity and suicidal behavior in adolescents would represent a significant public health concern, given the marked increases in prevalence of obesity among adolescents.
Cross-sectional relationships have been described repeatedly, but the extent to which body dissatisfaction, disordered eating, or obesity is predictive of the occurrence of suicidal behaviors over time is not known. Identifying factors that are predictive of suicide risk is essential for treatment and prevention. In this study, we examined the longitudinal relationships between body dissatisfaction, weight control behaviors, weight status, and self-reported suicidal behavior using a nonclinical sample of adolescents and young adults. To expand on previous work, we examined a larger group of both male and female adolescents over a longer period of time at multiple ages. We hypothesized that body dissatisfaction, weight control behaviors, and obesity would be positively associated with self-reported suicidal ideation and suicide attempts at 5-year follow-up.
Method Participants
Data for this analysis came from Project EAT-II (Neumark-Sztainer, Wall, Eisenberg, Story, & Hannan, 2006; Neumark-Sztainer, Wall, Guo, et al., 2006). Project EAT-II was a follow-up study of adolescents who had participated in Project EAT (Neumark-Sztainer, Story, Hannan, & Croll, 2002; Neumark-Sztainer, Story, Hannan, Perry, & Irving, 2002), which had examined dietary intake, weight status, eating behaviors, and socioenvironmental and demographic correlates in a large, ethnically diverse study population. Project EAT involved 4,746 Minnesota middle and high school students who were initially surveyed during 1998–1999. In Project EAT-II, we aimed to recontact all of the original participants 5 years after the initial study. A total of 1,074 (22.6%) of the original cohort was lost to follow-up, mainly because no address was found at follow-up (n = 591) or contact information was missing at Time 1 (n = 411). Of the original participants, 3,672 were contacted by mail in 2003–2004 and 2,516 (68.4% of those contacted; 1,386 female and 1,130 male) completed the Project EAT-II survey. The follow-up sample was more likely to be White and at higher socioeconomic status (SES); likely, this distribution was due to higher mobility in the lower SES and non-White groups, which were heavily represented in the original EAT sample. We used population weights that reflected the original sample in all analyses to address this shortcoming. About one third of the sample was in the high-school-age cohort (mean age = 17.2 years), and two thirds of the sample was in the young adult group (mean age = 20.4 years). The sample was ethnically diverse and comprised Asian Americans (19.2%), African Americans (18.7%), Hispanics/Latinos (5.8%), Native Americans (3.6%), and individuals of mixed race (4.3%); the remainder (48.4%) were Whites. A wide socioeconomic distribution was seen: Most participants fell in the lower middle, middle, or upper middle SES categories; 17.4% were of low SES, and 13.8% were of high SES. Further details of Project EAT-II are available elsewhere (Neumark-Sztainer, Wall, Eisenberg, et al., 2006; Neumark-Sztainer, Wall, Guo, et al., 2006). All study procedures for both Project EAT and Project EAT-II were approved by the University of Minnesota's Human Subject Committee.
Measures
Weight status
At Time 1, weight and height were measured by trained Project EAT staff in school settings. Weight was measured in light clothing, and height was measured without shoes. Body mass index (BMI; weight [kg]/height squared [m2]) was calculated, and participants were classified as being underweight (<15th BMI percentile), average weight (15th–85th BMI percentile), moderately overweight (85th–95th BMI percentile), or very overweight (≥95th BMI percentile), according to gender- and age-based cutpoints recommended by the Centers for Disease Control and Prevention (Kuczmarski et al., 2000).
Body satisfaction
Participants completed a 10-item scale (Pingitore, Spring, & Garfield, 1997) on which satisfaction with separate body parts and characteristics was rated from 1 (very dissatisfied) to 5 (very satisfied). For example, an item on the scale asks “How satisfied are you with your stomach?” A score ranging from 10 to 50 was created; greater scores indicated greater levels of body satisfaction (Pingitore et al., 1997). Cronbach's alpha at Time 2 was .92 for young women and .93 for young men for the composite score. In this study, test–retest reliability was acceptable, with Pearson correlations of .68–.77 in a racially diverse subgroup of 252 7th- and 10th-grade participants. The median score was 35; the top quartile cutoff was 41, and the lowest quartile cutoff was 28. In the current analyses, the lowest quartile was considered to have “body dissatisfaction.”
Suicidal thoughts and behaviors
Students reported on both suicide attempts and suicidal ideation. The survey questions used included “Have you ever thought about killing yourself?” and “Have you ever tried to kill yourself?” Response options included “no,” “yes, in the past year,” and “yes, more than a year ago.”
Those who reported past-year suicidal ideation or attempts at Time 2 were categorized as cases, regardless of their Time 1 suicidal status. Those who reported suicidal ideation or attempts “more than a year ago” at Time 2 but no suicidal reports at Time 1 were also categorized as cases. All others were considered to have no suicidal thoughts or behaviors at Time 2. In this way, we were able to identify suicidal ideation and attempts that had occurred after the assessment of Time 1 predictor variables. Two-week test–retest Spearman correlations at Time 1 were .78 (ideations) and .80 (attempts).
UWCB
The UWCB assessed included (1) taking diet pills, (2) “making myself vomit,” (3) using laxatives, (4) using diuretics, (5) fasting, (6) eating very little food, (7) using a food substitute (powder/special drink), (8) skipping meals, and (9) smoking more cigarettes. For each item, participants were asked to answer “yes” or “no” to “Have you done any of the following things in order to lose weight or keep from gaining weight during the past year?” Participants who endorsed any of Items 1–4 were classified as using extreme weight control behaviors (EWCB); participants who endorsed any of Items 5–9 were considered to have UWCB.
Depressive symptoms
We used a six-item scale to assess depressive symptoms (Kandel & Davies, 1982). At Time 2, the scale had a Cronbach's alpha of .80 for young men and .81 for young women. For the current analyses, those who scored in the highest quartile were considered to have high levels of depressive symptoms.
Demographic variables
SES was categorized into five levels with an algorithm based on highest educational level completed by either parent plus eligibility for free or reduced-price lunch, eligibility for public assistance, and parental employment status (Neumark-Sztainer, Story, Hannan, & Croll, 2002). Race/ethnicity was assessed by asking “Do you think of yourself as (a) White, (b) Black or African American, (c) Hispanic or Latino, (d) Asian American, (e) Hawaiian or Pacific Islander, (f) American Indian or Native American, or (g) other race?” Participants were asked to check all that applied. For purposes of this analysis, all non-Whites were grouped together. Analyses were adjusted for race/ethnicity on the basis of this grouping.
Data Analysis
Data were weighted to adjust for differential response rates with the response propensity method (Little, 1986), in which the inverse of the estimated probability that an individual would respond at Time 2 was used as the weight. Estimates were therefore generalizable to the population represented by the original Time 1 Project EAT sample.
We used separate multiple logistic regression analyses to estimate odds ratios for Time 2 suicidal ideation and attempts. In unadjusted analyses, four Time 1 weight-related variables (weight status, body dissatisfaction, EWCB, and UWCB) were entered simultaneously. A second model adjusted for race, SES, and age group. This model was further adjusted for high depressive symptoms at Time 2, given that depression has been associated with suicidal behavior.
ResultsWeight distribution, frequencies of EWCB and UWCB at Time 1, suicidal behavior at Time 2, and participant characteristics are shown in Table 1. UWCB were endorsed by the majority of young women (57.0%) and a large percentage of the young men (31.1%). EWCB were less common (12.9% of young women and 3.9% of young men). Suicidal ideation was reported by 21.6% of young women (12.6% in the past year; 8.9% more than 1 year earlier) and by 15.2% of young men (8.3% in the past year; 7.0% more than 1 year earlier) at Time 2, whereas suicide attempts were reported by 8.7% of young women and 3.5% of young men at Time 2.
Demographics and Key Variables by Gender
The unadjusted and adjusted relationships between Time 1 weight-related variables (weight status, body dissatisfaction, EWCB, and UWCB) and Time 2 suicidal ideation are shown in Table 2. For young women, EWCB were predictive of later suicidal ideation (odds ratio [OR] = 1.98, 95% confidence interval [CI] = 1.34–2.93). These ORs remained elevated even after we had adjusted for demographic variables and Time 2 depressive symptoms (OR = 1.79, 95% CI = 1.19–2.71). In contrast, among young men, the relationship between EWCB and suicidal ideation was not statistically significant. Furthermore, UWCB, body dissatisfaction, and weight status at baseline each failed to predict suicidal ideation at follow-up for male or female participants.
Weight Status, Body Dissatisfaction, and Weight Control Behaviors at Time 1 and Suicidal Ideation at Time 2
Table 3 shows the relationship between Time 1 weight status, body dissatisfaction, weight control behaviors, and Time 2 reported suicide attempts. Similarly, EWCB in young women was associated with a significantly elevated OR for suicide attempts (OR = 2.53, 95% CI = 1.53–4.18). These ORs remained elevated after we had adjusted for demographic variables and Time 2 depressive symptoms (OR = 2.41, 95% CI = 1.43–4.07). No association between suicide attempts and EWCB was found among young men. As with suicidal ideation, UWCB, body dissatisfaction, and weight status were not significantly associated with suicide attempts among either male or female participants over 5-year follow-up.
Weight Status, Body Dissatisfaction, and Weight Control Behaviors at Time 1 and Suicide Attempts at Time 2
DiscussionThe results of this study show that, in young women but not in young men, EWCB at baseline were predictive of suicidal ideation and suicide attempts at 5-year follow-up independent of depressive symptoms. Contrary to our hypotheses, body dissatisfaction, UWCB, and weight status were not predictive of suicidal behavior 5 years later in male or female participants.
These findings are consistent with the results of several previous studies that have shown an association between both syndromal eating disorders (Harris & Barraclough, 1994) and limited eating disorder symptoms and suicidal behaviors (Crow et al., 2008; Miotto, De Coppi, Frezze, & Preti, 2003). Previous studies were cross-sectional, however, and the current study indicates that EWCB are predictive of suicidal ideation and suicide attempts over time. Our results suggest that EWCB might be a risk factor or risk marker for later suicidality. Although the rates of suicidal ideation and attempts endorsed by participants were high, they were in the range of those reported in other community-based studies (Centers for Disease Control, 2000; Kessler et al., 1999).
Differential associations between EWCB and suicidal thoughts and behaviors were found between young men and young women. The smaller sample size of young men endorsing EWCB (as well as suicidal behaviors) and the resulting limited power to detect such efforts may explain this finding, given that the ORs observed were similar in male and female participants (but confidence intervals were larger in the former). An alternative explanation may be that differing societal attitudes regarding the importance of weight and shape make EWCB more emotionally salient for women. If this were the case, EWCB might have had more psychopathological consequences for young women than for young men.
Results from the current study differ from previous cross-sectional work, which has found an association between body dissatisfaction and suicidal behavior in adolescents (Crow et al., 2008; Miotto et al., 2003). The reasons for these divergent findings are unclear. Perhaps body dissatisfaction is associated with psychosocial distress that might have short-term but not long-term links to suicidal behavior. This study, with reassessment after 5 years, did not examine short-term relationships between these variables. Alternatively, body dissatisfaction and UWCB may be so common among young women at present as to be considered normative; perhaps only more severe psychopathology (e.g., weight control behaviors defined as “extreme”: purging, laxative use, and diuretic use) is predictive of suicidal behavior. This relationship might operate at the personality trait level. For example, underlying impulsivity has been associated with both disordered eating and suicidal behavior. Recent work has suggested that altered serotonin function may be correlated with the co-occurrence of self-harm, impulsivity, and bulimic symptoms (Steiger et al., 2001).
The results of this study further our understanding of the relationship between body weight and suicidal behavior. The finding that weight was unrelated to suicidal behavior is consistent with some prior work in this area (Crow et al., 2008; Hallstrom & Noppa, 1981; Kittel et al., 1978; Mukamal, Kawachi, Miller, & Rimm, 2007; Neumark-Sztainer, Story, Resnick, & Blum, 1997) but contradicts other studies (Carpenter et al., 2000; Falkner et al., 2001; Neumark-Sztainer, Story, Resnick, & Blum, 1997). Although the cross-sectional prior studies have been mixed, it is noteworthy that the two prospective studies examining this question (Mukamal et al., 2007, and the current study) have not found higher BMI to be predictive of suicide.
There are a number of strengths and some limitations to the current project. The sample size was large and diverse, in terms of gender, ethnicity, and SES. In addition, the use of a prospective design with 5-year follow-up broadened our understanding of the relationship between EWCB and later suicidal behavior. Limitations include the lack of extensive detail regarding suicidal ideation and suicide attempts. It would be helpful to have more information in this regard, particularly on the timing, frequency, and severity of suicidal behaviors over the 5-year period. There is evidence that varying the method of suicide assessment leads to variation in the reported rate of suicidal behavior (Prinstein, Nock, Spirito, & Grapentine, 2001). Similarly, more detailed measures of body dissatisfaction and disordered eating behaviors/attitudes would have been helpful and might have yielded different findings. Attrition at 5-year reassessment is another limitation. Finally, although our study was prospective, it nonetheless entailed some degree of retrospective recall at the 5-year reassessment point.
These findings have important implications for future research and practice. They help clarify the link between disordered eating and suicidal behavior. These findings further emphasize the importance of screening for EWCB. Levels of disordered eating that may fall short of diagnostic thresholds have been viewed as less serious, but such disordered eating is tied to psychosocial and physical morbidity and, this study suggests, is associated with an increased risk of suicidal behavior. It is unclear from the current study whether EWCB lead to suicidal thoughts/behaviors through some underlying mechanism or rather are a sign of more global distress and a risk marker for suicide risk. In either case, the use of EWCB by an adolescent suggests a need for careful monitoring of the adolescent's mental health. Future research should examine this issue over even longer periods of observation. In addition, it would be of interest to know whether the risk for adolescent suicidal behavior conferred by EWCB carries over into adulthood. If the link between EWCB and suicidal behavior is confirmed, efforts directed at preventing the onset of disordered eating might diminish the risk of subsequent suicidal behavior.
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Submitted: August 8, 2007 Revised: April 14, 2008 Accepted: April 25, 2008
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Source: Journal of Consulting and Clinical Psychology. Vol. 76. (5), Oct, 2008 pp. 887-892)
Accession Number: 2008-13625-017
Digital Object Identifier: 10.1037/a0012783
Record: 8- Title:
- Behavioral activation for depressed breast cancer patients: The impact of therapeutic compliance and quantity of activities completed on symptom reduction.
- Authors:
- Ryba, Marlena M., ORCID 0000-0001-6963-4034. Department of Psychology, The University of Tennessee– Knoxville, Knoxville, TN, US
Lejuez, C. W.. Department of Psychology, The University of Maryland, College Park, MD, US
Hopko, Derek R.. Department of Psychology, The University of Tennessee–Knoxville, Knoxville, TN, US, dhopko@utk.edu - Address:
- Hopko, Derek R., Department of Psychology, The University of Tennessee– Knoxville, 307 Austin Peay Building, Knoxville, TN, US, 37996-0900, dhopko@utk.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 82(2), Apr, 2014. pp. 325-335.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- behavioral activation, breast cancer, depression, mediation, process of change, therapeutic compliance, environmental reward
- Abstract:
- Objective: Behavioral activation (BA) is an empirically validated treatment that reduces depression by increasing overt behaviors and exposure to reinforcing environmental contingencies. Although research has identified an inverse correlation between pleasant or rewarding activities and depression, the causal relation between increased structured activities and reduced depression has not directly been studied. Method: In the context of a recent randomized trial (Hopko, Armento, et al., 2011), this study used longitudinal data and growth curve modeling to examine relationships among the quantity of activities completed, proportion of activities completed (i.e., therapeutic compliance), environmental reward, and depression in breast cancer patients treated with BA treatment for depression (n = 23). Results: Therapeutic compliance with assigned activities was causally related to depression reduction, whereas the specific quantity of completed activities was not systematically related. Logistic regression indicated that for patients completing all assigned activities, treatment response and remission were achieved for all patients. Neither therapeutic compliance nor the quantity of completed activities was directly associated with self-reported environmental reward during the BA interval (Session 3 to posttreatment), and environmental reward did not mediate the relation between activation and depression. Conclusions: Patient compliance with BA assignments is causally associated with depression reduction, whereas the quantity of completed activities is less relevant toward conceptualizing positive treatment outcome. Study findings are discussed in the context of behavioral models of depression and BA therapy. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Behavior Therapy; *Breast Neoplasms; *Major Depression; *Treatment Compliance; *Treatment Outcomes; Environment; Rewards
- Medical Subject Headings (MeSH):
- Adult; Aged; Behavior Therapy; Breast Neoplasms; Depressive Disorder; Female; Humans; Middle Aged; Patient Compliance; Reinforcement (Psychology); Treatment Outcome
- PsycINFO Classification:
- Behavior Therapy & Behavior Modification (3312)
- Population:
- Human
- Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Anxiety Disorders Interview Schedule–IV
Beck Depression Inventory–II DOI: 10.1037/t00742-000
Environmental Reward Observation Scale DOI: 10.1037/t51595-000
Hamilton Rating Scale for Depression DOI: 10.1037/t04100-000 - Grant Sponsorship:
- Sponsor: Susan G. Komen for the Cure Research
Grant Number: BCTR0706709
Recipients: Hopko, Derek R. - Methodology:
- Empirical Study; Longitudinal Study; Quantitative Study; Treatment Outcome
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Dec 23, 2013; Accepted: Nov 12, 2013; Revised: Oct 31, 2013; First Submitted: Jun 6, 2013
- Release Date:
- 20131223
- Correction Date:
- 20170323
- Copyright:
- American Psychological Association. 2013
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0035363
- PMID:
- 24364801
- Accession Number:
- 2013-44755-001
- Number of Citations in Source:
- 50
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-44755-001&site=ehost-live">Behavioral activation for depressed breast cancer patients: The impact of therapeutic compliance and quantity of activities completed on symptom reduction.</A>
- Database:
- PsycINFO
Behavioral Activation for Depressed Breast Cancer Patients: The Impact of Therapeutic Compliance and Quantity of Activities Completed on Symptom Reduction
By: Marlena M. Ryba
Department of Psychology, The University of Tennessee–Knoxville
C. W. Lejuez
Department of Psychology, The University of Maryland, College Park
Derek R. Hopko
Department of Psychology, The University of Tennessee–Knoxville;
Acknowledgement: This research was supported by Susan G. Komen for the Cure Research Grant BCTR0706709 awarded to Derek R. Hopko.
Behavioral activation (BA) is the therapeutic process of increasing overt behaviors to facilitate exposure to reinforcing environmental contingencies and subsequent reductions in depression (Hopko, Lejuez, Ruggiero, & Eifert, 2003). BA has evolved significantly in recent decades (Dimidjian, Barrera, Martell, Munoz, & Lewinsohn, 2011; Hopko, Ryba, McIndoo, & File, in press), and with growing empirical support, BA is now considered an empirically validated intervention and is an appealing treatment option for depression across a range of settings (Cuijpers, van Straten, & Warmerdam, 2007; Ekers, Richards, & Gilbody, 2008; Sturmey, 2009). There is still much to be learned about the process of change in BA, however, and no systematic longitudinal research has explored whether increased activation and environmental reinforcement are in fact central mediators of change. Accordingly, in the context of a recently completed randomized trial of BA and problem-solving therapy for depressed breast cancer patients (Hopko, Armento, et al., 2011), the current study was designed to more clearly explicate the relations among structured BA, environmental reward, and depression.
BA is rooted in behavioral models of depression that implicate decreased response-contingent positive reinforcement (RCPR) for nondepressive behavior as the causal factor in eliciting depression (Ferster, 1973; Lewinsohn, 1974). This reduction in RCPR is attributable to a decrease in the number and range of reinforcing stimuli available to an individual for such behavior, a lack of skill in obtaining reinforcement, and/or an increased frequency of punishment (Lewinsohn, 1974). This view suggests that depressed behavior results from a combination of reinforcement for depressed behavior and a lack of reinforcement or even punishment for more healthy alternative behaviors (Ferster, 1973; Hopko, Lejuez, et al., 2003; Lewinsohn, 1974). As a result, depressed individuals often experience significant behavioral inhibition and avoidance behaviors, the central target of contemporary behavioral treatments for depression: behavioral activation (BA; Martell, Addis, & Jacobson, 2001) and the brief behavioral activation treatment for depression (BATD; Lejuez, Hopko, & Hopko, 2001; revised version, BATD–R; Lejuez, Hopko, Acierno, Daughters, & Pagoto, 2011). Although BA approaches commonly are based on behavioral principles of reinforcement and functional assessment, specific treatment strategies differ across interventions (Kanter et al., 2010), with activity monitoring and activity scheduling being the two constant features of both BA protocols (Addis & Martell, 2004; Lejuez et al., 2001). The BA method (Martell et al., 2001) incorporates strategies of change including identification of avoidance patterns, teaching functional assessment of behavior, guided activity, mental rehearsal, periodic distraction, mindfulness training, rumination-cued activation, and skills training. Alternatively, BATD focuses on functional assessment of depressed behavior, identification of activities based on individualized goals and a life values assessment, and structured systematic activation using a hierarchy of activities. These contemporary versions of BA are considered more idiographic than conventional behavior therapy (Hopko, Lejuez, et al., 2003). Perhaps most important, BA and BATD moved away from the assumption that pleasant activities have reinforcing properties and instead focus on environmental contingencies maintaining depressed behavior, unique value systems, and the targeting of avoidance through an emotional acceptance and behavior change paradigm.
Compelling treatment outcome research suggests BA strategies have broad applicability across a wide range of settings and clinical populations. In one of the more compelling studies, BA was comparable to antidepressant medication and superior to cognitive therapy in treating severe depression (Dimidjian et al., 2006), results that were maintained at 2-year follow-up (Dobson et al., 2008). BA also has been effectively used with depressed patients in various settings and among individuals with coexistent medical problems such as HIV, cancer, brain trauma, and obesity, as well as coexistent psychiatric problems such as anxiety disorders, schizophrenia, and borderline personality disorder (see Hopko et al., in press, for a comprehensive review). Three independent meta-analyses support the efficacy of BA in treating depression (Cuijpers et al., 2007; Ekers et al., 2008; Mazzucchelli, Kane, & Rees, 2009).
BA models of depression attribute affective change to increases in RCPR for healthy behaviors. Several studies support this model, demonstrating a relationship between depressed mood and the frequency of pleasant activities and increased reinforcement (Grosscup & Lewinsohn, 1980; Hopko, Armento, et al., 2003; Lewinsohn & Libet, 1972; Lewinsohn & Shaffer, 1971; Lewinsohn & Shaw, 1969; MacPhillamy & Lewinsohn, 1974). In a study examining the relation of activation and depression using daily diary methods, self-reported depression was inversely related to general activity level as well as the level of self-reported reward or pleasure obtained through engaging in overt behaviors (Hopko, Armento, et al., 2003). Another recent study showed depressed college students engaged less frequently in social, physical, and educational behaviors (Hopko & Mullane, 2008). Although these cross-sectional data are significant, there continues to be a paucity of systematic longitudinal research that adequately demonstrates the process by which therapeutic effects are obtained in BA. The lack of research documenting a causal relationship between exposure to reinforcers and depression reduction was partially due to the lack of available statistical meditational analyses during early stages of BA research. Although three recent studies render support for a relationship between activation and reduced depression via meditational effects of environmental reinforcement (Carvalho & Hopko, 2011; Carvalho, Trent, & Hopko, 2011; Ryba & Hopko, 2012), none of these studies incorporated a sophisticated longitudinal design that allowed examination of a definitive temporal relationship between BA and reduced depressive affect.
A number of theoretical and empirical questions pertaining to BA remain unanswered and warrant continued scientific investigation to allow researchers to better conceptualize and refine behavioral treatments for depression. For example, although BA researchers and theorists implicate activity scheduling (and subsequent exposure to environmental reward) as the primary active component in BA, no systematic research has supported this hypothesis. Indeed, some versions of BA include many treatment components (e.g., skills training, rumination-cued activation, cognitive rehearsal) that raise speculation of whether alternate change mechanisms account for favorable treatment outcomes (Addis & Martell, 2004; Martell et al., 2001). Second, at this stage of BA research, it is largely unclear whether the specific quantity of behaviors or the proportion completed (i.e., treatment compliance) is more essential toward conceptualizing positive treatment outcome. Clarification of the process of change in BA would assist mental health providers and facilitate further BA treatment refinement by identifying components of BA that account for significant outcome variance. To address these issues, this study examined relationships among the quantity of activities completed, proportion of activities completed (i.e., therapeutic compliance), environmental reward, and depression reduction in breast cancer patient treated with eight sessions of BATD. Study hypotheses were as follows:
Hypothesis 1: As BATD progressed, the overall quantity of activities assigned was predicted to increase.
Hypothesis 2: As BATD progressed, treatment compliance (i.e., proportion of activities completed) was predicted to increase.
Hypothesis 3: The proportion of activities completed and reduced depression would be mediated by increased environmental reward.
Hypothesis 4: The quantity of activities completed and reduced depression would be mediated by increased environmental reward.
Hypothesis 5: Individuals completing a greater quantity of activities and greater proportion of assigned activities were predicted to achieve treatment response and remission at posttreatment.
Method Participants
Participants were 23 breast cancer patients with a diagnosis of major depression who were treated at the University of Tennessee Medical Center’s Cancer Institute as part of a randomized clinical trial (Hopko, Armento, et al., 2011). All participants provided informed consent prior to study enrollment. Patients were recruited through clinic screening, clinic brochures, and oncologist referral. Patients interested in study participation completed a pretreatment diagnostic assessment that included the Anxiety Disorders Interview Schedule–IV (ADIS–IV; Brown, Di Nardo, & Barlow, 1994) and self-report instruments outlined in the following section. Advanced doctoral students conducted psychological assessments and were supervised by the principal investigator (DH) in the context of audiotape review and discussion, resulting in a consensus diagnosis. Individuals were eligible to participate if they were older than age 18, had been diagnosed with breast cancer, had a principle (and primary) diagnosis of major depression, and were not psychotic or cognitively impaired. The clinical trial included 80 depressed breast cancer patients, of which 42 were assigned to BATD. For the purposes of this study, only BATD patients who completed and returned all behavioral activation monitoring logs were included (n = 23).
The majority of these patients were White (91.3%); 8.7% were African American. The mean age was 57 years (SD = 11.3), and the average length of education was 15.2 years (SD = 2.2). Marital status was as follows: married (56.5%), single (21.7%), divorced (17.4%), and separated (4.3%). As reported in the randomized trial (Hopko, Armento, et al., 2011), for purposes of generalizability, antidepressant and antianxiety medication usage was not exclusionary. In this sample, 11 patients (48%) were prescribed antidepressant or antianxiety medication, and all had been stabilized at a consistent dosage for 8 weeks prior to study assessment, with no variations in medications or dosages throughout the trial (as reported by patients prior to each BATD session). The average time since breast cancer diagnosis was 2.7 years (SD = 1.9), and average tumor size was 2.57 cm (SD = 0.5). Patients of all cancer stages were included: Stage 0 (lobular carcinoma in situ or ductal carcinoma in situ: n = 7, 30%); Stage 1 (n = 7, 30%); Stage 2 (n = 5, 22%); Stage 3 (n = 3, 14%); and Stage 4 (n = 1, 4%). In terms of cancer treatment, 87% of patients had surgery (i.e., lumpectomy, mastectomy), 64% had chemotherapy, and 70% had radiation treatment. All patients in this sample had successfully completed their respective cancer treatment regimen prior to commencing BATD. Important insofar as assessing representation of the entire BATD sample (n = 42), other than failing to maintain monitoring logs, a series of analyses of variance for continuous variables and chi-square analyses for categorical variables indicated that the study sample (n = 23) and holdout sample (n = 19) did not statistically differ on any demographic, cancer-related, or psychological variables, including treatment response, χ2(1) = 2.29, p = .20, and remission, χ2(1) = 0.02, p = .99, rates following BATD (see Hopko, Armento, et al., 2011), as well as pretreatment depression severity, anxiety severity, social support, environmental reward, or self-reported bodily pain. For pretreatment depression severity, the study sample and holdout group did not differ on either the Beck Depression Inventory–II (BDI–II; Beck, Steer, & Brown, 1996), t(78) = 0.38, p = .71, or the Hamilton Rating Scale for Depression (Hamilton, 1960), t(78) = 0.87, p = .39. The two samples also did not differ as a function of the number of coexistent anxiety disorders, t(78) = 0.75, p = .46.
Assessment Measures
Behavioral monitoring
In BATD, the master activity log is used by the clinician to track weekly patient progress. All activities are listed on the master activity log, including (a) the number of times the patient eventually would like to complete the activity in a 1-week period (i.e., ideal frequency) and (b) the duration of the activity. In the initial session of BATD, fewer activities are monitored, with the number of activities progressively increasing in subsequent weeks as a function of patient success. On the behavioral checkout that is maintained by the patient, the frequency and duration of goals for each week also are listed, and the patient records which behaviors are completed on a daily basis. The patient returns the behavioral checkout to the clinician each week, and the information is transferred to the master activity log. If the patient has achieved (or exceeded) goals, the clinician likely will increase the frequency and/or duration for the following week (assuming the patient has not met the ideal goal). If a behavioral assignment was not completed, the clinician and patient decide if the assignment was reasonable or whether it was excessive or improbable given the patient’s abilities. In the former case, the goal might be the same for the next week or its importance (i.e., consistency with life values) re-evaluated. In the latter case, the clinician and patient might strongly consider reducing the weekly goal, and potentially the ideal goal. The rate at which new activities are added can occur slowly or rapidly across patients and generally is determined based on individual circumstances.
Beck Depression Inventory–II
The BDI–II consists of 21 items rated on a 4-point Likert scale. The BDI–II has excellent reliability and validity in depressed adults (Beck et al., 1996). The psychometric properties of the BDI–II also have been studied in cancer patients and a medical care sample, with strong predictive validity as it pertains to a diagnosis of clinical depression, strong internal consistency (α = .94), and adequate item-total correlations (range = .54–.74; Arnau, Meagher, Norris, & Bramson, 2001; Katz, Kopek, Waldron, Devins, & Thomlinson, 2004; α = .84; range = 14–60; M = 27.0, SD = 8.5, for the present study).
Environmental Reward Observation Scale
The EROS (Armento & Hopko, 2007) is a 10-item measure assessing exposure to environmental rewards deemed essential for increasing response-contingent positive reinforcement (RCPR; Lewinsohn, 1974). RCPR is defined as positive or pleasurable outcomes that follow behaviors; the outcomes may be extrinsic (e.g., social, monetary) or intrinsic (e.g., physiological, feeling of achievement) and increase the likelihood the behaviors will occur in the future. Decreased RCPR is a central predictor of increased depression (Lewinsohn, 1974). Higher scores on the EROS suggest increased environmental reward. Sample items include “The activities I engage in have positive consequences,” and “Lots of activities in my life are pleasurable.” Based on psychometric research with three independent college samples, the EROS has strong internal consistency (α = .85–.86) and excellent test–retest reliability (r = .85) and correlates strongly with other psychometrically sound measures of depression (r = from –.63 to –.69) and anxiety (Armento & Hopko, 2007). In this study, internal consistency was adequate (α = .78; M = 22.7, SD = 4.6).
Behavioral Activation Therapy for Depression (BATD)
BATD focuses on increasing overt behaviors to bring patients into contact with reinforcing environmental contingencies and corresponding improvements in thoughts, mood, and quality of life (Hopko, Lejuez, et al., 2003). Within BATD (Hopko & Lejuez, 2007; Lejuez et al., 2001, 2011), the process of increasing RCPR follows the basic principles of extinction, shaping, fading, and in vivo exposure (Hopko, Lejuez, et al., 2003). Initial sessions involved assessing the function of depressed behavior, establishing patient rapport, motivational exercises focused on the pros and cons of behavioral change, depression and breast cancer psychoeducation, and introduction of the treatment rationale. Within BATD, systematically increased activity is a necessary precursor toward the reduction of overt and covert depressed behavior. Patients began with a self-monitoring (daily diary) exercise to examine already occurring daily activities to provide a baseline measurement and ideas of activities to target during treatment. Patients were asked to keep a daily diary during 4 days of the week and to monitor their primary overt behaviors at half-hour intervals (from 8:00 a.m. to 2:00 a.m.). For each behavior, they also were asked to indicate their level of reward or pleasure on a 4-point Likert scale. Following monitoring, emphasis shifted to identifying values and goals within life areas that included family, social, and intimate relationships; education; employment and career; hobbies/recreation; volunteer work/charity; physical/health issues; spirituality; and anxiety-eliciting situations (Hayes, Strosahl, & Wilson, 1999). An activity hierarchy was then constructed in which 15 activities were rated from “easiest” to “most difficult” to accomplish. Using the master activity log and behavioral checkout to monitor progress, patients progressively moved through the hierarchy, from easier behaviors to the more difficult. The process of assigning behavioral activation goals began in Session 3. Weekly goals were recorded on a behavioral checkout form that the patient returned to therapy each week. At the start of each session, the behavioral checkout was examined and discussed, with the following weekly goals established as a function of patient success or difficulty. Although not outside the scope of BATD, attention to ongoing cancer treatment or cancer survivor issues were not directly addressed, in the former case because all breast cancer patients in this sample were provided psychotherapy following cancer treatment. In total, BATD involved eight sessions of approximately 1 hr in duration.
Therapists and Treatment Integrity
Advanced clinical psychology (doctoral) students served as therapists in this study. All therapists were skilled in the administration of BATD, had been trained by the principal investigator (DH), and had been regularly practicing BATD with patients for a minimum of 2 years prior to the study. To ensure competent provision of BATD, we audiotaped all sessions, and all therapists met for weekly individual supervision meetings with the principal investigator (DH). A total of 15% of tapes were selected randomly for ratings of therapist competence and adherence by an independent evaluator with expertise in behavioral therapy. Ratings were made on Likert scales ranging from 0 (no adherence/competence) to 8 (complete adherence/competence) on a session-by-session basis, with ratings based on adherence and ability in completing session objectives highlighted in the BATD treatment manual. Consistent with the very high ratings previously reported for the entire BATD sample (Hopko, Armento, et al., 2011), ratings of sessions conducted with this patient sample indicated high therapist adherence (M = 7.2; SD = 0.7) and competence (M = 6.9; SD = 1.0) to the BATD protocol.
Procedure
Following recruitment and screening procedures, eligible participants were administered the ADIS–IV and all self-report measures. All psychological assessments and treatment sessions were conducted at the Cancer Institute. Advanced doctoral students in clinical psychology conducted the comprehensive assessments. Patients subsequently engaged in their 8-week (one-on-one) treatment. At the beginning of each session, the BDI–II and EROS were completed to assess depression and environmental reward. For the purposes of this study, the master activity logs and behavioral checkouts were reviewed to assess the number of activities assigned and the proportion of activities completed by each patient.
Statistical Analyses
BA assignments commenced in the third session, meaning that the impact of the quantity and proportion of activities completed as they related to depression and environmental reward began to be assessed at Week 4 of treatment through the posttreatment assessment. Accordingly, the longitudinal data consisted of six observations: Sessions 4 (Data Point 1) through 8 and posttreatment (Data Point 6)]. Growth curve modeling was used to test levels of change in a dependent variable over time and incorporated within-subject and between-subjects predictors. The general model was as follows:
The value of the dependent variable for patient i at time t was equal to a subject-specific intercept plus a subject-specific time slope. Setting t1 = 0, t2 = 1, t3 = 2, and so on; the intercept γ0i is the expected value for patient i at the first observation. If the expectation is that all patients have a similar baseline score that is not dependent on other variables, it can be written as a function of the overall average baseline across all individuals plus a random noise component representing person-specific differences from the mean.
The value of γ1i reflects how rapidly each patient’s score on the dependent variable changes over time. Because each patient has her own growth trajectory, differences in the intercept and slopes are also modeled by individual-specific covariates. For example, it is predicted that depression severity declines more quickly as patients engage in more activities. Thus, we can model γ0i as a function of total activities completed (or proportion completed).
Substituting:
where the coefficient
on the interaction tests the significance of total activities completed on time. Because there is an interaction, a main effect for total activities completed also is included. This is done by adding total activities completed to the model for the intercept.
This yields the final model:
The beta parameters represent fixed effects, or the average slopes and intercept across all patients in the sample. The r parameters represent random effects. They are not estimated as traditional regression coefficients. Rather, they are summarized by their variances as variance components. The larger the variance component, the greater the variability in growth trajectories between patients.
The previous model represents a between-subjects analysis, since it uses the total number of activities completed across time as the primary predictor. The data also contain week-specific values for the number of activities completed and proportion completed. Growth models also allow for the inclusion of time-varying predictors. In this case, the model simplifies to:
Due to perfect multicollinearity between the time-varying activities measure and the total activities completed variable, the within-subject model was tested separately from the between-subjects model. Both were estimated to determine whether there were differences between week-specific activities completed and the total number of activities completed across the assessment period. The models described were all estimated using the MIXED command in SPSS Version 20.
Finally, it is also possible to test for mediation using a growth curve model, although it is more complicated for longitudinal relative to cross-sectional data (Selig & Preacher, 2009). Testing mediation is simplified in within-subject analyses, since the between-subjects model involves an interaction. The process for within-subject data amounts to fitting a simultaneous equations model in which the activities variable is both a predictor of depression severity and an outcome determined by environmental reward. Due to the fact that both equations involve random effects (as they are both growth models with time-varying variables), this part of the estimation was done using Mplus (Version 6.1; Muthén & Muthén, 2007). Standard errors for the indirect effects were estimated using bootstrapping.
The first hypothesis predicted that the number of activities assigned would increase as therapy progressed. For this hypothesis, the number of activities at time point t was the dependent variable in a growth model, and time was the sole predictor. The second hypothesis predicted that general compliance (proportion of activities completed) would increase as therapy progressed. This assertion was tested in the same manner as the first hypothesis, except that proportion of activities completed was the dependent variable. The third hypothesis predicted that depression severity would systematically decrease as the proportion of activities completed increased. In this case, depression was the dependent variable in the growth model. In the between-subjects model, the total proportion of activities completed was the predictor, and its interaction with time was tested to determine if a higher level of compliance resulted in quicker reductions in depression. A within-subject analysis determined if variations in weekly compliance had short-term effects on depression. A mediation analysis assessed whether any observed relationship between compliance and depression was partially or fully accounted for by the relationship between compliance and environmental reward. The third hypothesis also predicted that compliance (proportion of activities completed) would lead to increased environmental reward. Due to the simultaneous equations used to test mediation in Hypothesis 3, this test was integrated into the same model. Specifically, the model included an equation in which environmental reward was a direct consequence of compliance. If the coefficient for the effect of compliance on reward was significant, Hypothesis four would be supported. The fourth hypothesis was similar to the third, except total activities completed, rather than proportion completed, was the dependent variable. The same tests for direct effects and mediation were conducted. The fifth hypothesis predicted that the proportion of activities and quantity of completed activities would significantly impact treatment response and remission. Because each of these dependent variables was measured on a dichotomous scale, logistic regression was used. The independent variables were total proportion of activities completed and total quantity of activities completed. Results are reported as odds ratios, where a one-unit increase in the proportion of activities completed (or quantity of activities completed) is associated with a β increase in the odds of response or remission.
Response and Remission Criteria
Consistent with methods highlighted in previous trials of BA (Dimidjian et al., 2006; Hopko, Armento, et al., 2011), response represented significant symptomatic improvement, whereas remission represented improvement to the point of being asymptomatic within normal range. On the BDI–II, response was defined as at least a 50% reduction from baseline. Remission was defined as scores ≤ 10 on the BDI–II.
ResultsSupporting the first hypothesis and consistent with the progressive framework of BATD, results indicated that the number of assigned activities and number of completed activities significantly increased over time. As illustrated in Figure 1, during the first week of BA assignments (i.e., following Session 3), the average number of assigned activities was 12 (95% confidence interval, or CI, [9.29, 14.62]), nearly doubling to 23.39 (95% CI [17.84, 28.94]) at the conclusion of treatment. Additionally, there was greater variability in the number of assigned activities at the end of treatment. The number of activities assigned was expected to increase weekly by 2.16 (SE = 0.49, p < .01). However, the rate of increase was higher for some individuals and lower for others (σ2 = 4.35, SE = 1.66, p = .01). There was also significant variability in the initial number of assigned activities (σ2 = 29.36, SE = 14.21, p = .04). Patients who initially had fewer assigned activities had more substantial increases throughout treatment. The trend of completed activities over time is highlighted in Figure 2. During the first week of treatment, the average number of completed activities was 12.91 (95% CI [9.90, 15.93]) and 22.56 (95% CI [17.03, 28.10]) in the final week. Patients on average completed an additional 1.9 (SE = 0.51, p < .01) activities each week.
Figure 1. Average number of assigned behaviors. CI = confidence interval.
Figure 2. Average number of completed behaviors. CI = confidence interval.
When examining Hypothesis 2, we found that results were nonsignificant and indicated that the proportion of activities completed did not systematically increase during the course of BATD (B = −0.01, SE = 0.01, p = .36). As illustrated in Figure 3, following the first assignment, the average proportion of activities completed was 1.12 (95% CI [0.95, 1.30]), compared with 1.00 (95% CI [0.85, 1.16]) following the final activation session. The proportion of activities completed was essentially unchanged over time, primarily due to a ceiling effect whereby all patients were largely compliant with behavioral activation assignments.
Figure 3. Proportion of completed behaviors. CI = confidence interval.
Results partially supported Hypotheses 3 and 4. Figure 4 displays depression severity as a function of treatment session. Significant patient improvement was evident across behavioral activation sessions, whereby during the first week, the average BDI–II score was 14.83 (95% CI [11.53, 18.13]), decreasing to 10.04 (95% CI [6.87, 13.21]) by the final week of BATD. Figure 5 also shows a trend for increased environmental reward over time as a function of behavioral activation, with initial environmental reward of 26.13 (95% CI [24.45, 27.81]) increasing to 29.22 (95% CI [27.34, 31.06]) by the final week of treatment. Between-subjects analysis demonstrated that the average proportion of activities completed was significantly associated with decreased depression (B = −13.53, SE = 6.97, p < .05). The interaction between average proportion of completed activities and time was tested to determine if higher levels of compliance led to faster reductions in depression. The interaction was not significant (B = −0.12, SE = 1.11, p = .91). When a similar model was applied with environmental reward as the dependent variable, the average proportion of activities completed did not have a significant main effect (B = 2.19, SE = 3.69, p = .56) or interaction with time (B = 0.33, SE = 0.68, p = .63).
Figure 4. Average depression (Beck Depression Inventory–II; BDI–II) scores. CI = confidence interval.
Figure 5. Average Environmental Reward Observation Scale scores. CI = confidence interval.
Within-subject analysis incorporated week-specific values for proportion of completed activities. The results reinforce findings from the between-subjects model. As the proportion of activities completed in a given week increased, depression severity also decreased for that week (B = −4.04, SE = 1.40, p < .01). This result was observed beyond the general trend of diminishing depression severity, which was also significant (B = −1.24, SE = 0.27, p < .01). Thus, higher levels of compliance with behavioral activation assignments were effective in attenuating depression. As in the between-subjects model, the within-subject model showed that compliance did not have an effect on environmental reward. That is, the proportion of activities completed in a given week was not significantly related to that week’s score on the environmental reward scale (B = 0.94, SE = 0.81, p = .25), although the time trend was significant (B = 0.65, SE = 0.12, p < .01). With no evidence that proportion of completed activities had a significant effect on environmental reward, it was unlikely that the relationship between proportion of completed activities and depression was mediated by environmental reward. A multilevel mediation model confirmed there was no indirect effect (B = .026, SE = 0.037, p = .48).
When the total number of completed activities rather than proportion of completed activities was the primary predictor, quantity of completed activities did not affect depression severity either directly (B = −0.03, SE = 0.05, p = .53) or through its interaction with time trend (B = 0.00, SE = 0.01, p = .70). Only the time trend was significant in the model, with average depression severity decreasing each week (B = −1.48, SE = 0.78, p < .05). Similarly, findings showed a null effect of total completed activities on environmental reward. The main effect of total activities completed was not significant (B = 0.01, SE = 0.02, p = .71), and neither was the interaction with time (B = 0.00, SE = 0.00, p = .89). This was also demonstrated in the within-subject models, with total activities completed in a given week having no effect on that week’s depression score (B = 0.00, SE = 0.06, p = .94) and no effect on the respective week’s environmental reward score (B = −0.02, SE = 0.03, p = .59). With no effect of quantity of completed activities on depression severity or environmental reward, the key components of mediation were absent. Estimating a mediation model in Mplus yielded a nonsignificant within-subject indirect effect of −0.966 (SE = 1.128, p = .39), confirming the lack of mediation.
Logistic regression was used to examine Hypothesis 5, whether treatment response and remission were more likely to occur with a greater proportion of activities completed and greater quantity of activities completed. For treatment response, the average proportion of activities completed was significant (−21.19, SE = 9.33, p = .02). When patients completed all assigned activities, BDI–II treatment response was evident across all patients, exp (B) < .01, (95% CI [.00, .06]). The high pseudo R2 values (Cox & Snell = .44, Nagelkerke = .64) also suggested very strong effect sizes. For depression remission, results were again significant (B = −9.97, SE = 4.95, p = .04). The odds ratio suggested that for those patients who completed all assigned activities, the odds of not achieving remission were essentially zero, exp (B) < .01 (95% CI [0.00, 0.77]). Model fit statistics indicated that the proportion of activities completed was a good predictor of remission outcomes (Cox & Snell = .27, Nagelkerke = .38). No effect was found when considering the predictive power of quantity of completed activities on BDI–II treatment response, B = −0.01, SE = 0.01, p = .55; exp (B) = .99 (95% CI [0.97, 1.02]), or remission, B = .00, SE = 0.01, p = .79; exp (B) = 1, (95% CI [0.98, 1.02]). Thus, relative to quantity of activities completed, treatment compliance was a better predictor of treatment outcome.
DiscussionContemporary BA treatments aim to attenuate depression via increased RCPR. Although specific intervention strategies differ across BA protocols, structured activation assignments are common to all approaches. This study examined longitudinal data to better explicate the process of change in BATD via growth curve modeling and examining relationships among the quantity of activities completed, proportion of activities completed (i.e., therapeutic compliance), environmental reward, and depression reduction. Findings demonstrate that while the average number of assigned and completed activities systematically increased over time, there was no progressive change in therapeutic compliance, with overall compliance being exceptionally high throughout psychotherapy. Extraordinary patient compliance with BATD may be reflective of a number of patient-centered, therapy-related, and social and economic factors, and potentially high therapist competence in assigning and reviewing homework (Jin, Sklar, Oh, & Li, 2008; Weck, Richtberg, Esch, Hofling, & Stangier, 2013). This finding is significant in that it supports the feasibility and tolerability of BATD for patients presenting with complex clinical presentations including a coexistent psychological disorder and medical illness.
When examining the effect of therapeutic compliance and quantity of completed activities on depression, study hypotheses were partially supported. Results indicated that from pre- to posttreatment, depression decreased as patients completed a higher proportion of activities. On a more microanalytical level, results revealed systematic reductions in depression during weeks where therapeutic compliance was highest. Somewhat unexpectedly, there was no significant effect on depression as a function of increased quantity of completed activities. Therefore, results suggest patient adherence to behavioral assignments is more critical in reducing depression relative to simply completing a greater number of activities. In fact, when examining the impact of therapeutic compliance on treatment response and remission, results were highly compelling and emphasize the importance of patient compliance toward positive BATD treatment outcome. Indeed, patient compliance with activation assignments resulted in favorable BATD treatment outcome. In terms of understanding BATD process of change, completing a greater proportion of assigned activities not only presumably increases RCPR but also likely facilitates greater self-efficacy, accomplishment, and mastery within valued life areas, and subsequent reductions in depression.
The finding of a limited relationship between quantity of activities completed and depression reduction is somewhat inconsistent with previous research showing a positive correlation between activity level and elevated mood. Accounting for this finding, it is conceivable that as the quantity of behavioral assignments increases, their significance pertaining to identified life values decreases. Indeed, initial behavioral assignments are not only prescribed according to their level of difficulty but also their relevance toward addressing the most important life values and their significance toward achieving the most principal life goals. Accordingly, a greater breadth of assigned behaviors may involve activities becoming more generic, less salient in terms of being value based and directly related to immediate life goals, and consequently less apt to result in environmental reinforcement and decreased depression. Another possible explanation may be related to the process of activity scheduling that involves assigning activities of progressively increasing difficulty (e.g., they require more time or effort, require underdeveloped skills, are associated with more anxiety, and avoidance motivation is stronger). With increased behavioral assignments, this process may involve inclusion of activities with less potential reinforcement value and possibly greater likelihood of aversive or unpleasant experiences and consequences that might prevent depression attenuation (e.g., lack of success, heightened anxiety). Highly important to acknowledge in this sample of breast cancer survivors, a committed engagement in fewer but highly valued (and possibly less difficult) activities might be the preferred mode of behavioral activation as patients seek to extract meaning in life, often following a very demanding breast cancer treatment regimen.
Analyses of the role of environmental reward revealed unexpected results given previous studies demonstrating the meditating effects of reinforcement on the relationship between behavioral activation and depression (Carvalho, Trent, et al., 2011; Carvalho & Hopko, 2011; Ryba & Hopko, 2012). Current findings yielded no support for either the proportion or quantity of completed activities as significantly related to self-reported environmental reward. However, study limitations should be taken into account when interpreting this finding, particularly the self-report method of assessing reinforcement. Because direct measurement of reinforcement would require direct observations of environmental contingencies across time and is not overly pragmatic, the practice of using self-report strategies to assess environmental reward, pleasure, and reinforcement is the common alternative. As such, environmental reward as measured in the current study may be an inadequate proxy for actual response-contingent positive reinforcement experienced in the natural environment.
Important to highlight, BA and increased environmental reinforcement likely is not the only critical mediator of change in BATD. For example, the sudden gain literature suggests the beneficial effects of BATD are at least partially independent of the activation process itself, with 50% of sudden gains, or large symptom improvements between one treatment interval, occurring prior to the activation process that commences in Session 3 (Hopko, Robertson, & Carvalho, 2009; Hunnicutt-Ferguson, Hoxha, & Gollan, 2012; Kelly, Cyranowski, & Frank, 2007; Tang & DeRubeis, 1999). This means that sudden gains may be partially reflective of developing therapeutic alliance, psychoeducation, environmental modification (i.e., reducing reinforcement for depressed behavior), and structured value assessment that occur in the first two BA sessions. Additionally, it is conceivable that sudden gains partially reflect self-activation in the absence of therapist guidance. In a recent study of sudden gains in depressed patients receiving BA (Hunnicutt-Ferguson et al., 2012) and consistent with previous data (Hopko et al., 2009), 67% of sudden gains occurred in the first few sessions of behavioral activation. In the present study, although increased proportion of activation assignments completed directly mediated depression reduction, it also is true that BDI–II depression severity had already decreased 45% from baseline (i.e., from moderate to mild depression) prior to beginning structured activation in Session 3. This early and significant reduction in depression symptoms prior to activation emphasizes the need to further examine nonspecific therapy factors such as patient motivation for treatment, perceived support, previous therapy experiences, or patient-specific risk/protective factors such as chronicity of depression and comorbid diagnoses (Hunnicutt-Ferguson et al., 2012). The important point here is that in the context of methodological problems with directly measuring environmental reinforcement and frequently large reductions in depression that occur prior to activation, environmental reinforcement may remain highly operative as a mediator in BATD—although other important mediators of change most definitely must be considered.
Although findings are provocative, several important study limitations are noteworthy. First, a larger sample size would have increased power and confidence in study findings. For example, although mediation models have been examined in smaller samples, a larger sample size would have allowed for better assessment of the potential mediating effects of environmental reward (Fritz & MacKinnon, 2007). Second, as this study followed from a randomized clinical trial and was not specifically designed to account for all possible mediators of change, future work should assess the impact of common factors on treatment outcome (e.g., therapeutic alliance, level of therapist reinforcement, patient self-efficacy). Third, it is conceivable that study recruitment strategies yielded a unique cohort of breast cancer patients that on some unmeasured variable may have been distinct from the population of breast cancer patients. Fourth, as discussed, the ideal method of assessing reinforcement via direct observations of environmental contingencies was not feasible, resulting in the use of a self-report measure of environmental reward (EROS). As “environmental reward” is not synonymous with “environmental reinforcement,” no definitive conclusion can be drawn about the relationship of reinforcement with treatment response to BATD. In addition, as the EROS was designed to assess environmental reward within “the past several months” but was administered weekly in this study, this measure characteristic could have complicated data interpretation and possibly contributed to Type II error in examining environmental reward as a mediating variable. Although still not optimal, the Reward Probability Index (RPI; Carvalho, Gawrysiak, et al., 2011) may have been a preferred proxy measure of environmental reinforcement as unlike the EROS, the RPI better assesses the construct of response-contingent positive reinforcement (i.e., number of reinforcers, availability of reinforcers, ability to obtain reinforcement, exposure to aversive events; Lewinsohn, 1974). Fifth, although BATD practitioners in this study had been trained by the senior author and had been educated in the principles and practice of BATD for a minimum of 2 years, they certainly would not be qualified as experts given their status as unlicensed clinical practitioners.
Sixth, daily diary logs were used to track activity assignments and completion. Although patients received careful instruction and reported procedural adherence, it cannot be definitively stated that activities were logged reliably or accurately. Seventh, because only 55% of the original sample of patients treated with BA (Hopko, Armento, et al., 2011) returned all behavioral monitoring logs, this analyzable subsample may not be entirely representative of the entire BA cohort or breast cancer patients in general. Although this subsample and holdout sample (i.e., those not returning monitoring logs) did not statistically differ on all primary psychological, demographic, and cancer-related study variables, had comparable levels of pretreatment depression severity, and had similar response and remission rates to BATD, it is conceivable that between-group differences existed on some unmeasured variable(s). As many anecdotal therapist reports and audiotaped session transcripts indicated, however, a majority of (holdout) BATD patients who did not successfully return behavioral monitoring logs to therapy sessions nevertheless communicated strong adherence to weekly activation assignments. Nonetheless, significant findings cannot be generalized to the holdout sample or population of breast cancer patients with absolute confidence. Eighth, the study examined the quantity and proportion of completed activities but did not differentiate among types of activities (e.g., social, physical, educational). Categorization of activities may have provided a more detailed understanding of whether engagement in certain types of activities was relevant in the process and outcome of BA. Finally, since the study examined process of change among a highly educated and largely White sample of depressed breast cancer patients, further inquiry into the generalizability of study findings to other patient samples is warranted.
In closing, study findings provide novel insight into the process and outcome of BA and have important clinical and research implications. Results highlight the efficacy of BA and suggest therapeutic compliance is a vital component toward increasing the probability of positive treatment outcome. Highly provocative, findings also suggested that “more” behavioral activation as defined by an increased quantity of completed behaviors does not necessarily correspond with improved treatment response. Further research should more systematically examine dose-response relationships associated with BA while also being mindful of whether certain categories or types of activities are more relevant toward conceptualizing treatment outcome. Given the empirical support and practicality of BA, continued investigation of process factors is warranted. Dismantling studies of treatment components of differing BA approaches also may be beneficial toward better isolating strategies most essential to engendering healthy behaviors. Although in the present study medication use was not examined in primary analyses (but used as a covariate) due to findings that medication use was unassociated with treatment response and remission in this sample (Hopko, Armento, et al., 2011), because combined therapy may be associated with improved treatment response in some depressed patients (Hollon et al., 2005), this issue also should be further examined in understanding the process and outcome of BA. In addition, although factors associated with treatment failure in behavior activation have been discussed that include noncompliance with behavioral assignments (Hopko, Magidson, & Lejuez, 2011), strategies most effective in promoting compliance with activation are largely unknown and require further investigation. The uncomplicated and easily disseminated approach of BA has many potential applications in a broad range of clinical settings and by a diverse network of providers (Ekers, Richards, McMillan, Bland, & Gilbody, 2011). The more precisely the process of change is understood, the better equipped researchers and clinicians will be to further refine and deliver the most parsimonious and efficacious form of BA.
Footnotes 1 As reported, the study and holdout samples did not significantly differ on any primary psychological, demographic, and cancer-related variables. Although sophisticated statistical procedures such as multiple imputation for missing data were considered, because of excessive missing data (64%) in the holdout sample on the behavioral monitoring logs on which patients were to record compliance with assigned activities, multiple imputation procedures were not used as doing so would have violated statistical assumptions (Little & Rubin, 2002; Rubin, 1987) and potentially rendered data invalid and non-interpretable.
2 All growth curve modeling and mediation analyses also were conducted using medication status (i.e., not medicated, stabilized on medication) and pretreatment depression severity as covariates. Results remained consistent with those reported.
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Submitted: June 6, 2013 Revised: October 31, 2013 Accepted: November 12, 2013
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Source: Journal of Consulting and Clinical Psychology. Vol. 82. (2), Apr, 2014 pp. 325-335)
Accession Number: 2013-44755-001
Digital Object Identifier: 10.1037/a0035363
Record: 9- Title:
- Case complexity as a guide for psychological treatment selection.
- Authors:
- Delgadillo, Jaime. Clinical Psychology Unit, Department of Psychology, University of Sheffield, Sheffield, United Kingdom, jaime.delgadillo@nhs.net
Huey, Dale. Primary Care Psychological Therapies Service, Greater Manchester West Mental Health NHS Foundation Trust, Salford, United Kingdom
Bennett, Hazel. Primary Care Psychological Therapies Service, Greater Manchester West Mental Health NHS Foundation Trust, Salford, United Kingdom
McMillan, Dean. Hull York Medical School, University of York, United Kingdom - Address:
- Delgadillo, Jaime, Clinical Psychology Unit, University of Sheffield, Cathedral Court, Floor F, 1 Vicar Lane, Sheffield, United Kingdom, S1 1HD, jaime.delgadillo@nhs.net
- Source:
- Journal of Consulting and Clinical Psychology, Vol 85(9), Sep, 2017. pp. 835-853.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 19
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- psychotherapy, stratified medicine, mental health, case complexity
- Abstract (English):
- Objective: Some cases are thought to be more complex and difficult to treat, although there is little consensus on how to define complexity in psychological care. This study proposes an actuarial, data-driven method of identifying complex cases based on their individual characteristics. Method: Clinical records for 1,512 patients accessing low- and high-intensity psychological treatments were partitioned in 2 random subsamples. Prognostic indices predicting post-treatment reliable and clinically significant improvement (RCSI) in depression (Patient Health Questionnaire-9; Kroenke, Spitzer, & Williams, 2001) and anxiety (Generalized Anxiety Disorder-7; Spitzer, Kroenke, Williams, & Löwe, 2006) symptoms were estimated in 1 subsample using penalized (Lasso) regressions with optimal scaling. A PI-based algorithm was used to classify patients as standard (St) or complex (Cx) cases in the second (cross-validation) subsample. RCSI rates were compared between Cx cases that accessed treatments of different intensities using logistic regression. Results: St cases had significantly higher RCSI rates compared to Cx cases (OR = 1.81 to 2.81). Cx cases tended to attain better depression outcomes if they were initially assigned to high-intensity (vs. low intensity) interventions (OR = 2.23); a similar pattern was observed for anxiety but the odds ratio (1.74) was not statistically significant. Conclusions: Complex cases could be detected early and matched to high-intensity interventions to improve outcomes. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Impact Statement:
- What is the public health significance of this article?—Complex cases tend to have a poor prognosis after psychological treatment for depression and anxiety problems. An evidence-based model of defining complexity is proposed to guide therapists in matching patients to treatments of differing intensity. The findings indicate that this personalized method of treatment selection could lead to better outcomes for complex cases and could improve upon decisions that are informed by clinical judgment alone. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Mental Health Services; Psychotherapy; Treatment
- PsycINFO Classification:
- Health & Mental Health Services (3370)
- Population:
- Human
Male
Female - Location:
- United Kingdom
- Age Group:
- Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs)
Aged (65 yrs & older)
Very Old (85 yrs & older) - Tests & Measures:
- Standardized Assessment of Personality–Abbreviated Scale
Work and Social Adjustment Scale
Generalized Anxiety Disorder 7 DOI: 10.1037/t02591-000
Patient Health Questionnaire-9 DOI: 10.1037/t06165-000 - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: May 29, 2017; Revised: May 16, 2017; First Submitted: Nov 14, 2016
- Release Date:
- 20170831
- Copyright:
- American Psychological Association. 2017
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/ccp0000231
- Accession Number:
- 2017-36111-001
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2017-36111-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2017-36111-001&site=ehost-live">Case complexity as a guide for psychological treatment selection.</A>
- Database:
- PsycINFO
Case Complexity as a Guide for Psychological Treatment Selection
By: Jaime Delgadillo
Clinical Psychology Unit, Department of Psychology, University of Sheffield;
Dale Huey
Primary Care Psychological Therapies Service, Greater Manchester West Mental Health NHS Foundation Trust, Salford, United Kingdom
Hazel Bennett
Primary Care Psychological Therapies Service, Greater Manchester West Mental Health NHS Foundation Trust, Salford, United Kingdom
Dean McMillan
Hull York Medical School and Department of Health Sciences, University of York
Acknowledgement: We thank Jan R. Böhnke for helpful comments on an earlier version of this article.
A commonly held view in clinical psychology is that complex cases require suitably intensive interventions guided by formulations that account for obstacles to improvement (Tarrier, 2006). Clinical wisdom reflected in treatment textbooks suggests that a variety of factors can complicate treatment, such as chronic symptoms, comorbidity, personality disorders, physical illnesses, and so forth (Beck, 1998; Hawton, Salkovskis, Kirk, & Clark, 1989; Tarrier, Wells, & Haddock, 1998). Along these lines, Ruscio and Holohan (2006) proposed a list of more than 40 factors that characterize complex cases, clustered around several themes including symptoms, safety, physical, intellectual, personality, and other features. Evidently, case complexity is a heterogeneous concept and there is little consensus about the features that define such cases.
Moreover, the empirical literature casts doubt over the predictive value of many variables presumed to hinder the effectiveness of therapy (Garfield, 1994). A case in point is found in the study by Myhr et al. (2007), in which only five out of 10 variables thought to be indicative of poor suitability for cognitive therapy were (weakly) correlated with post-treatment outcomes. It is also well documented that clinicians’ prognostic assessment of patients tends to be inaccurate (Ægisdóttir et al., 2006; Grove & Meehl, 1996), often failing to identify cases at risk of poor treatment outcomes (Hannan et al., 2005). In another study, patients randomly assigned to brief manualized interventions offered in a stepped care model had comparable outcomes to patients whose treatments were selected and informed by clinical judgment (Van Straten, Tiemens, Hakkaart, Nolen, & Donker, 2006). Such evidence calls into question clinicians’ ability to match patients to treatments and supports current guidelines to apply a stepped care approach (National Institute for Health and Care Excellence [NICE], 2011). Overall, three key problems are apparent: a lack of conceptual clarity about complex cases, a gap between clinical judgment and research evidence, and limitations in clinicians’ ability to identify and select optimal treatments for complex cases.
Concerns regarding complexity are not exclusive to the practice of psychotherapy. The simultaneous growth and ageing of the general population have confronted many other areas of health care with the challenges of treating patients who present with multiple chronic conditions (Smith & O’Dowd, 2007), leading some to question the usefulness of evidence-based guidelines that are formulated for “prototypical” patients (Boyd et al., 2005; Tinetti, Bogardus, & Agostini, 2004). Consequently, theoretical models to account for case complexity in medicine have been proposed in the last decade. Some of these models conceptualize complexity as arising from a combination of clinical (e.g., diagnostic), biological, socioeconomic, cultural, environmental, and behavioral factors that are statistically associated with clinical prognosis (Safford, Allison, & Kiefe, 2007; Schaink et al., 2012). Individual patients may have protective or risk factors across these domains, and their overall complexity level results from the sum of risks. In an attempt to move beyond mere description, Shippee, Shah, May, Mair, and Montori (2012) proposed a cumulative complexity model that attempts to explain how risk factors accumulate and interact to influence health care outcomes. They proposed that (clinical, socioeconomic, cultural) risk factors complicate health care outcomes by disrupting the balance between patient workload (i.e., number and difficulty of daily life demands including self-care) and patient capacity (i.e., resources and limitations affecting ability to meet demands). From this perspective, effective health care for complex cases would not only require intensive treatment of acute symptoms and specific disease mechanisms, but also attending to wider biopsychosocial aspects that may redress the balance between demands and capacity. Common to these models are the focus on empirically supported prognostic factors, the consideration of factors across multiple domains, and the conceptual understanding of case complexity as resulting from the accumulation of risks and challenges to self-management.
Informed by these theoretical models emerging from the biomedical sciences, this study investigated the impact of case complexity in routine psychological care. Considering the problems outlined above, we sought to assess the merits of an actuarial, data-driven, cumulative model of defining case complexity. Specific objectives were (a) to identify prognostic variables associated with psychological treatment outcomes, (b) to develop an algorithm that could aid clinicians in identifying complex cases at risk of poor outcomes, (c) to determine whether or not complex cases respond differentially to treatments of differing levels of intensity, (d) to ascertain the extent to which patients are adequately matched to available stepped care interventions.
Method Setting and Interventions
This study was based on the analysis of clinical data routinely collected by a primary care psychological therapy service in Northern England. The study was approved as a service evaluation by the local National Health Service (NHS) Trust, which did not require formal ethical approval. The service offered low- and high-intensity interventions for depression and anxiety problems, as part of the Improving Access to Psychological Therapies (IAPT) program (Clark et al., 2009). Low-intensity treatments (LIT) consisted of brief (<8 sessions lasting 30 min) psychoeducational interventions based on principles of cognitive behavioral therapy (CBT). These were highly structured interventions, supported by didactic materials and delivered by a workforce of psychological wellbeing practitioners trained to a standard national curriculum (Bennett-Levy et al., 2010). High-intensity treatments (HIT) were lengthier (up to 20 sessions lasting around 60 min) interventions including CBT and counseling for depression. These interventions were also protocol-driven, delivered by postgraduate level counselors and psychotherapists, following national treatment guidelines (NICE, 2010) and competency frameworks (e.g., Roth & Pilling, 2008). All therapists practiced under regular clinical supervision (weekly or fortnightly) to ensure ethical practice and treatment fidelity. These interventions were organized in a stepped care model (NICE, 2011), where most patients initially accessed LIT and those with persistent and/or severe symptoms accessed HIT. Initial treatment assignment was determined by therapists who carried out standardized intake assessments.
Measures and Data Sources
Primary outcome measures
Patients accessing IAPT services self-complete standardized outcome measures on a session-to-session basis to monitor response to treatment. The Patient Health Questionnaire-9 (PHQ-9; Kroenke et al., 2001) is a nine-item screening tool for major depression, where each item is rated on a 0 to 3 Likert scale, yielding a total depression severity score between 0 and 27. A cut-off ≥10 has been recommended to detect clinically significant depression symptoms (Kroenke et al., 2001), and a difference of ≥6 points between assessments is indicative of reliable change (Richards & Borglin, 2011).
The Generalized Anxiety Disorder-7 (GAD-7; Spitzer, Kroenke, Williams, & Löwe, 2006) is a seven-item measure developed to screen for anxiety disorders. It is also rated using Likert scales, yielding a total anxiety severity score between 0 and 21. A cut-off score ≥8 is recommended to identify the likely presence of a diagnosable anxiety disorder (Kroenke, Spitzer, Williams, Monahan, & Löwe, 2007), and a difference of ≥5 points is indicative of reliable change (Richards & Borglin, 2011). Pre-treatment and last observed PHQ-9 and GAD-7 scores were available for analysis.
Other measures
The Work and Social Adjustment Scale (WSAS; Mundt, Marks, Shear, & Greist, 2002) is a measure of functioning across five domains: work, home management, social leisure activities, private leisure activities, family and close relationships. Each item is rated on a scale of 0 (no impairment) to 8 (very severe impairment), rendering a total functional impairment score between 0 and 40.
The Standardized Assessment of Personality–Abbreviated Scale (SAPAS) is an eight-item questionnaire developed to screen for the likely presence of a personality disorder (Moran et al., 2003). Each question prompts respondents to endorse specific personality traits (yes/no), yielding a total score between 0 and 8 where a cut-off >3 is indicative of cases with a high probability of diagnosable personality disorders. The WSAS and SAPAS were gathered at the time of initial assessments.
De-identified treatment and demographic data were also available, including information on referral sources, the intensity and sequence of treatments received (LIT and/or HIT along the stepped care pathway), age, gender, ethnicity and employment status. Formal diagnostic assessments were not carried out in routine care, but primary presenting problems noted in clinical records were available in summary form as group-level percentages.
Sample Characteristics
The study included case records for a total of 2,202 patients who had been discharged from the service at the time of data collection. Complete data (described earlier) were available for 1,512 (68.7%) cases. More than half were females (63.9%), with a mean age of 41.99 (SD = 14.54; range = 16 – 87) and of white British ethnic background (88.2%). A quarter (24.9%) of all cases were unemployed and/or in receipt of incapacity benefits. Approximately 59.9% were referred to treatment by general medical practitioners; the remainder self-referred (24.3%) or were referred by other social and health care providers (15.8%). The presenting problems noted in clinical records were depression (21.0%), recurrent depression (6.6%), obsessive–compulsive disorder (4.4%), adjustment disorders (5.7%), somatoform disorders (0.4%), eating disorders (0.4%), phobic disorders (5.7%), other anxiety disorders (42.4%), and unspecified mental health problems (13.4%). Mean baseline severity scores for the whole cohort were PHQ-9 = 14.86 (SD = 6.33), GAD-7 = 13.27 (SD = 5.07), WSAS = 18.39 (SD = 9.46), SAPAS = 3.82 (SD = 1.89; cases with SAPAS >3 = 54.2%). Many patients had comorbid presentations, where 71.4% of cases had case-level symptoms in both PHQ-9 and GAD-7. Approximately 76.6% of patients were initially assigned to LIT and 23.4% were initially assigned to HIT. Overall, 40.6% only accessed LIT, 36.0% accessed LIT + HIT, and 23.4% only accessed HIT. Overall, 31.3% dropped out of treatment (32.2% of those initially assigned to LIT; 28.5% of those initially assigned to HIT).
Statistical Analysis
Consistent with the objectives of the study, data analyses were performed in four stages aiming to develop, validate and assess the clinical utility of a cumulative complexity model. The primary analyses were carried out in the dataset of cases with complete data (N = 1,512). Following a cross-validation approach, we partitioned this dataset into two random halves which were treated as estimation (N = 755) and validation (N = 757) samples. In order to assess the potential influence of missing data, a single imputed estimation sample (N = 1,108) was derived using an expectation-maximization method (Schafer & Olsen, 1998) and was used for sensitivity analyses described below.
Stage I involved the development of a prognostic index and classification method to identify complex cases in routine care. The dependent variable in all models was a binary indicator of post-treatment reliable and clinically significant improvement (RCSI), with separate models for depression (PHQ-9) and anxiety (GAD-7) measures. RCSI was determined using the criteria proposed by Jacobson and Truax (1991), based on combining reliable change indices for PHQ-9 (≥6) and GAD-7 (≥5) described by Richards and Borglin (2011) and diagnostic cut-offs for each measure (PHQ-9 < 10; GAD-7 < 8). The dependent variable was coded as follows: 0 = RCSI; 1 = no RCSI, such that the prognostic models would be constructed to identify (more complex) cases with increased probability of poor outcomes.
As an initial variable screening step, we used univariate logistic regressions to examine the goodness-of-fit (based on −2 log likelihood test and magnitude of AIC and BIC statistics) of linear and nonlinear trends for continuous variables, as well as alternative ways to model the SAPAS Questionnaire (as a total score, dichotomized based on a cut-off >3, or entered as a series of eight binary items). Entering all eight SAPAS binary items yielded the best fitting models in preliminary tests (i.e., lowest AIC and BIC, significant −2 log likelihood tests) and confirmed that only five items were significant (p < .05) predictors of outcome. Furthermore, baseline severity (PHQ-9, GAD-7), impairment (WSAS) and age variables were optimally modeled using nonlinear trends. Age was rescaled to ordinal decade groups (e.g., teens, twenties, thirties, etc.) and reverse scored (oldest group coded 0, youngest group coded 6) on the basis of the observed trend of correlations between age and RCSI.
Informed by these preliminary tests, we applied penalized categorical regressions with optimal scaling (CATREG-Lasso) in the main analysis. CATREG applies classical linear regression to predictor variables that are transformed to categorical quantifications which are optimally suited to explore nonlinear relations in the data (Gifi, 1990). Continuous variables were thus transformed using a monotonic spline scaling level to examine nonlinear associations with the dependent variable. Variable selection and regularization were performed combining the Lasso procedure (Tibshirani, 1996) and the .632 bootstrap resampling method (Efron & Tibshirani, 1997). The Lasso imposes a penalty term that shrinks coefficients toward zero, penalizing the sum of the squared regression coefficients. This yields more generalizable prediction equations compared with conventional regression models which are prone to overfitting and are less reliable in the presence of multicollinearity. Because using different penalty terms results in different shrunken coefficients, resampling techniques are often used to determine an optimal penalty. The .632 bootstrap resampling method is a smoothed version of the leave-one-out cross-validation strategy, which permits the estimation of a model’s expected prediction error. This resampling method was applied 1,000 times to each Lasso model, iteratively increasing the penalty term in 0.01 units, until all coefficients were shrunk to zero. The one-standard-error rule was applied to select the most parsimonious Lasso model within one standard error of the model with minimum expected prediction error. The predictors entered into CATREG-Lasso models included clinical (baseline PHQ-9, GAD-7, WSAS), personality (SAPAS items 1, 2, 3, 5, 7) and demographic variables (age groups, gender, ethnicity, employment status). Shrunken coefficients from the optimal models were used to calculate a prognostic index (PI) for each patient, where a higher PI denotes poorer prognosis. PIs were retained in the CATREG quantifications scale, with signed and continuous scores centered at zero.
The above procedure was conducted in the estimation samples with complete and imputed data, allowing us to compare the area under the curve (AUC) for the PIs derived from each dataset as an indicator of predictive accuracy. PIs derived using complete and imputed samples had comparable AUC statistics albeit with some shrinkage observed in the imputed dataset (PHQ-9: 0.67 ± 0.04 vs. 0.63 ± 0.05; GAD-7: 0.74 ± 0.04 vs. 0.66 ± 0.04). Therefore, subsequent analyses were applied in the dataset with complete data.
In Stage II, we applied receiver operating characteristic (ROC) curve analysis (Altman & Bland, 1994) in the estimation sample to determine empirical cut-offs that optimally balanced sensitivity and specificity on each PI. Consistent with our assumptions about clinical complexity, cases where both (PHQ-9 and GAD-7) PIs were above empirical cut-offs were classed as complex (Cx), and others (including all those with subclinical symptoms) were classed as standard (St) cases. The agreement of both PIs was taken as a conservative means of minimizing “false positive” classifications, and limiting the Cx classification to cases with the poorest prognoses across both outcome domains. We then tested our assumptions about prognosis and cumulative complexity in the validation subsample, with cases whose symptoms were above diagnostic cut-offs for each outcome measure (PHQ-9: N = 675; GAD-7: N = 755). ROC curve analyses were used to assess how well the PIs (using Lasso-based shrunken coefficients from the estimation sample) performed out-of-sample (in a statistically independent validation sample). In addition, separate logistic regression models were applied for each outcome (PHQ-9, GAD-7), where the dependent variable was post-treatment RCSI status (0 = no RCSI; 1 = RCSI) and the predictors included case complexity (0 = Cx, 1 = St) controlling for baseline severity of symptoms (PHQ-9 or GAD-7, respectively).
Stage III analyses were also conducted in the validation sample. A logistic regression model predicting (HIT vs. LIT) group membership based on clinical and demographic characteristics was performed to estimate propensity scores, denoting the predicted probability of completing a treatment episode at HIT. Propensity scores were entered as a covariate in subsequent analyses to control for confounding by indication. Next, logistic regression models were applied with RCSI status as a dependent variable, entering baseline severity (PHQ-9 or GAD-7, respectively), propensity scores, and treatment pathway (LIT or HIT only vs. LIT + HIT) as predictors. The models were performed separately in the subgroups of Cx (N = 269) and St (N = 425) cases (with available data to estimate propensity scores), to minimize multicollinearity between propensity scores and case complexity dummy variables in the same model.
In Stage IV, we assessed the extent to which initial treatment assignment (LIT or HIT) determined by clinical judgment was consistent with the assignment that would be indicated by the prognostic method described previously. A prognostic treatment assignment was coded for all patients, where starting at HIT was recommended for Cx cases and starting at LIT was recommended for all other cases. Next, agreement codes were noted for each case in the full sample, where “1” indicated agreement between clinical judgment and prognosis, and “0” indicated disagreement. Agreement codes were aggregated across the entire sample to estimate a “hit rate,” denoting the percentage of cases where clinicians’ decisions converged with a prognostic strategy for treatment assignment. Next, we applied Cohen’s kappa across agreement codes to derive a Treatment Matching precision (TMaP) score, which takes into account the probability that “hit rates” may be due to chance. The TMap score is therefore a robust measure of convergence between clinical and empirical decision-making strategies, ranging between 1 (perfect agreement) and −1 (complete disagreement), where 0 is indicative of agreement by chance. TMaP scores were estimated for the full sample and for individual clinicians that undertook initial assessments and made decisions about treatment assignment for at least 20 patients (to eschew extreme scores in caseloads with small base rates).
Results Estimation of Prognostic Equations
Using the CATREG-Lasso procedure in the estimation sample, we arrived at prognostic models that explained between 9% (PHQ-9: optimal scaling adjusted R2 = 0.09) and 15% (GAD-7: adjusted R2 = 0.15) of variance in posttreatment RCSI. Regression and ROC curve model estimates for each outcome measure are presented in Table 1 (with detailed outputs in Appendix A). Several predictors were selected into optimal Lasso models, including demographic (age, ethnicity, employment), personality (SAPAS items: 2 = interpersonally avoidant, 3 = suspicious, 5 = impulsive, 7 = dependent), and clinical features (baseline PHQ-9, GAD-7, WSAS).
Estimation of Prognostic Indices Using Penalized Categorical Regression With Optimal Scaling
The R2 share statistic reflects the relative contribution of each predictor to the model’s overall adjusted R2, after partialing out the specific and combined effects of the other variables. In the depression model, demographics had relatively greater explanatory influence (22.5%) relative to personality (14.7%) and clinical features (15%), although the remaining R2 variance was large (47.9%) and reflected the combined influence of all variables in the model. In the anxiety model, clinical features (55.9%) had two to three times greater explanatory power relative to personality (23.9%) and demographic features (15.2%), leaving only 5% of the remaining R2 variance to combined effects. The F tests for specific variables in both models suggested that the removal of clinical factors (particularly PHQ-9) significantly deteriorated the predictive power of regression models. AUC statistics for the depression (0.67, SE = 0.02) and anxiety (0.74, SE = 0.02) prognostic indices applied to predict RCSI in the estimation sample were both statistically significant (p < .001); ROC curves are shown in Appendix B.
Validation of Case Complexity Model
PIs using the shrunken coefficients derived from the estimation sample were applied in the validation sample, yielding stable and statistically significant (p < .001) AUC estimates for depression (0.64, SE = 0.02) and anxiety (0.70, SE = 0.02) measures (see ROC curves in Appendix B). Overall, 28.6% of all patients were classified as Cx by the prognostic classification rule derived using ROC curve analyses. The proportion of Cx cases was lower in the subsample of patients who only accessed LIT (15.9%) by comparison to those who accessed LIT + HIT (37.3%) and those who only accessed HIT (36.7%); χ2(2) = 97.05, p < .001.
As illustrated in Figure 1, logistic regression models (see Table 2) confirmed that St cases were significantly more likely to attain RCSI in depression (OR = 1.81) and anxiety (OR = 2.81) symptoms compared with Cx cases, after controlling for baseline severity.
Figure 1. Reliable and clinically significant improvement (RCSI) in cases classified as standard (St) and complex (Cx).
Validation of Prognostic Indices Applied Out-of-Sample-Using Logistic Regression
Case Complexity and Treatment Selection
Logistic regression models presented in Table 3 indicated that Cx cases had a significantly greater probability of RCSI in depression symptoms if they directly accessed HIT, by comparison to a standard stepped care pathway LIT + HIT (OR = 2.23, p = .01). There was also a trend indicating the same advantage of HIT for Cx cases in the anxiety model, although this did not reach statistical significance (OR = 1.74, p = .08). No significant differences were found between treatment pathways in the regression models applied to St cases. These analyses controlled for baseline symptom severity and propensity scores (derived from logistic regression model in Appendix C). The results for the depression outcomes are illustrated in Figure 2; where Cx cases that were initially assigned to HIT (optimal prognostic treatment assignment) had a 16.3% increased probability of RCSI by comparison to Cx who were assigned to a conventional stepped care pathway (LIT + HIT).
Logistic Regression Models Assessing Case Complexity and Treatment Selection
Figure 2. Reliable and clinically significant improvement (RCSI) in cases classified as standard (St) and complex (Cx) according to treatment pathway.
Clinical Judgment Versus Prognostic Models
The aggregated hit rate in the full sample indicated that clinicians’ treatment assignment decisions agreed with the prognostic strategy in 65.6% of cases. The TMaP score for the full sample, however, was low (κ = 0.09, SE = 0.02, p < .001). A closer examination of individual therapists’ treatment assignment decisions (N = 1,247 nested within 26 therapists) revealed considerable variability in their hit rates (range = 36.5% to 84.7%; M = 62.9, SD = 14.3) and TMaP scores (range = −0.27 to 0.44; M = 0.05, SD = 0.20). As shown in Figure 3, hit rates and TMaP scores were moderately correlated (r = .67, p < .001), and approximately 48% of therapists had TMaP scores <0.
Figure 3. Distribution of hit rates and treatment matching precision (TMaP) scores across 26 therapists.
Discussion Main Findings
This study set out to contribute to the understanding of case complexity in psychological care, in view of the limited conceptual clarity and evidence base surrounding this topic. Our findings demonstrate that (a) several patient characteristics have a cumulative effect on treatment outcomes, (b) it is possible to make reasonably accurate prognoses using this information, and (c) prognostic models can help us to operationalize case complexity in a way that is clinically useful. Cases classed as Cx (28.6%) on the basis of prognostic data tended to have significantly poorer outcomes after psychological treatment. Furthermore, Cx cases were two times (OR = 2.23) more likely to attain RCSI in depression symptoms if they were initially assigned to a high-intensity intervention instead of usual stepped care. A similar trend was observed for anxiety symptoms, although this did not reach statistical significance.
A Conceptual Bridge Between Prognosis and Case Complexity
These results lend support to the clinical notion that some cases are more difficult to treat due to various complicating factors (Ruscio & Holohan, 2006), although clinicians’ intuitions and treatment planning are often inconsistent with the evidence base (Garb, 2005). We found that treatment assignment decisions guided by clinical judgment were consistent with prognostic models in 65.6% of cases. This rate of agreement could be achieved by chance, or simply by mechanically following stepped care guidelines and assigning all cases initially to LIT, because the base rate of Cx cases is relatively low (under 30%). This was evidenced more clearly by examining the aggregated TMaP score (0.09) which was close to zero. Overall, the findings indicate that depression improvement (RCSI) rates for Cx cases could be significantly increased (by approximately 16.3%) if clinical judgment was supported by prognostic treatment selection models.
This gap between practice and science is perhaps accentuated by an unwieldy literature on the topic of prognosis in psychological care. Previous authors have attempted to synthesize findings across multiple studies to elucidate predictors of depression and anxiety outcomes (e.g., Driessen & Hollon, 2010; Haby, Donnelly, Corry, & Vos, 2006; Hamilton & Dobson, 2002; Keeley et al., 2008; Kessler et al., 2017; Licht-Strunk et al., 2007; Nilsen, Eisemann, & Kvernmo, 2013). Although some convergent findings are evident, meta-analytic reviews that privilege data from clinical trials are limited by typically small samples with sparse and heterogeneous prognostic variables, often gathered in highly selected participants (i.e., those with specific disorders) that may not be representative of complex cases seen in routine care (Chambless & Ollendick, 2001). Naturalistic cohort studies can offer informative evidence to complement findings from controlled trials, especially where multiple variables are measured systematically across large health care populations, as exemplified in this study. Several such studies are yielding replicated findings (e.g., Beard et al., 2016; Delgadillo, Moreea, & Lutz, 2016; Delgadillo, Dawson, Gilbody, & Böhnke, 2017; Firth, Barkham, Kellett, & Saxon, 2015; Goddard, Wingrove, & Moran, 2015; Licht-Strunk et al., 2009).
Overall, the emerging literature on outcome prediction points to factors clustered around clinical (i.e., baseline symptom severity, diagnosis, comorbidity, functioning and disability, physical illnesses), demographic (i.e., age, ethnicity, employment, socioeconomic deprivation, marital status), characterological (i.e., personality disorder diagnoses or traits, interpersonal problems and style, trait anxiety and neuroticism), and dispositional domains (i.e., readiness to change, expectancy). Informed by advances in the biomedical literature (Safford et al., 2007; Shippee et al., 2012), we propose that complex cases in psychological care are characterized by the presence of measurable factors that map onto multiple domains (clinical, demographic, characterological and dispositional), which are statistically associated with clinical prognosis and have a cumulative—detrimental—effect on treatment outcomes. The concept of case complexity is, therefore, dimensional (i.e., degrees of complexity on a continuum), and complex cases can be distinguished from others using empirically derived population norms and classification rules.
Case complexity may challenge psychological improvement through several mechanisms. One possibility is that an accumulation of disadvantages (e.g., poverty, interpersonal difficulties, functional impairment, outgroup derogation due to minority ethnic status) could disrupt the balance between life stressors and coping resources (Shippee et al., 2012). Complexity could also interfere with adequate engagement with therapy, for example, by undermining expectancy, which is a well-established predictor of treatment outcomes (Constantino et al., 2011). Baseline severity is an important contributor to complexity, so another possibility is that high baseline severity does not completely block improvement but may dampen the effect of treatment (i.e., cases with high severity can attain reliable improvement even if their symptoms do not reach subclinical levels). Furthermore, our findings suggest that specific features (i.e., demographic, clinical, characterological) influence specific clinical outcomes (remission of depression, anxiety) differentially. For example, demographic factors (e.g., young age, unemployment) had a considerably larger influence over depression outcomes relative to clinical and characterological factors. Future research could focus on exploring the relative contribution of different prognostic domains to multiple outcome domains (symptoms, quality of life, functioning) and the mechanisms through which these cumulative disadvantages may complicate or undermine treatment.
Limitations
Some limitations should be considered when interpreting the results of this study. As is common in naturalistic data sets, we encountered several cases with missing data (>30%). To deal with this, we applied multiple imputation and sensitivity analyses that yielded similar prognostic models, albeit with some shrinkage observed in the imputed dataset. On this basis, it was appropriate to perform further validation analyses using cases with complete data, to simulate how prognostic assessments would be applied in routine care, where data imputation of missing values is unfeasible.
Another limitation concerning the data used in this study was that we only had access to pre-post outcome measures for the entire treatment pathway, and it was not possible to disaggregate the outcomes for LIT and HIT for cases that accessed both steps. However, we were able to determine that Cx that only accessed HIT tended to have better outcomes compared to those who accessed LIT + HIT (a lengthier and costly treatment pathway). This suggests that there are no benefits of having LIT sessions preceding HIT, and hence the advantage of being initially assigned to HIT may not be solely due to having a lengthier treatment episode. Previous research using more granular outcomes data for each treatment step suggested that cases with poor prognostic features had a higher probability of dropout and lower probability of improvement at the LIT step by comparison to HIT (Delgadillo et al., 2016). These emerging findings suggest that assigning complex cases directly to HIT seems justified, although future randomized controlled trials of this strategy are necessary to determine if it is indeed more cost-effective.
Other limitations include the lack of formal diagnostic assessments and the analysis of a limited number of prognostic variables. It is known, for example, that specific diagnoses such as post-traumatic stress disorder, eating disorders and obsessive–compulsive disorder are associated with poorer outcomes in stepped care services (Delgadillo et al., 2017), and it is plausible that such diagnoses could interact with other prognostic features. Notwithstanding these limitations, it is remarkable that this narrow range of variables yielded an accurate and clinically useful prognostic model. Other studies using routine practice data have shown that similar variables can be used to identify subgroups of cases with depression and anxiety problems that attain similar outcomes (Delgadillo et al., 2016; Lutz, Lowry, Kopta, Einstein, & Howard, 2001; Lutz et al., 2005; Saunders, Cape, Fearon, & Pilling, 2016).
Clinical Implications
In line with recent findings in stepped-care psychological treatment settings (Delgadillo et al., 2016; Lorenzo-Luaces, DeRubeis, van Straten, & Tiemens, 2017), the present study provides further evidence that applying prognostic indices to guide personalized treatment recommendations is likely to improve treatment outcomes. Low-intensity guided self-help interventions are recommended as first-line treatments for several common mental disorders (NICE, 2011) and are becoming widely available in routine stepped care services (Clark, 2011). The application of evidence-based treatment selection algorithms like the one demonstrated in this study could help to maximize the cost-effectiveness of LIT by selectively offering it to those who are most likely to derive benefits. Equally, prognostic models could be used to fast-track complex cases to HIT in a timely way.
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APPENDICES APPENDIX A: Penalized Categorical Regression Models With Optimal Scaling
ANOVA for PHQ-9 Model (Estimation Sample)
Figure A1. Lasso paths for Patient Health Questionnaire–9 (PHQ-9) model. See the online article for the color version of this figure.
Lasso-Based Model Coefficients
Figure A2. Categorical quantifications and transformation plots for Patient Health Questionnaire–9 (PHQ-9) model (significant predictors only).
ANOVA for GAD-7 Model (Estimation Sample)
Figure A3. Lasso paths for Generalized Anxiety Disorder–7 Scale (GAD-7) model. See the online article for the color version of this figure.
Lasso-Based Model Coefficients
Figure A4. Categorical quantifications and transformation plots for Generalized Anxiety Disorder–7 Scale (GAD-7) model (significant predictors only).
ANOVA for PHQ-9 Model (Estimation Sample)
Figure A1. Lasso paths for Patient Health Questionnaire–9 (PHQ-9) model. See the online article for the color version of this figure.
Lasso-Based Model Coefficients
Figure A2. Categorical quantifications and transformation plots for Patient Health Questionnaire–9 (PHQ-9) model (significant predictors only).
ANOVA for GAD-7 Model (Estimation Sample)
Figure A3. Lasso paths for Generalized Anxiety Disorder–7 Scale (GAD-7) model. See the online article for the color version of this figure.
Lasso-Based Model Coefficients
Figure A4. Categorical quantifications and transformation plots for Generalized Anxiety Disorder–7 Scale (GAD-7) model (significant predictors only). APPENDIX B: Receiver Operating Characteristic (ROC) Curves
Figure B1. Patient Health Questionnaire–9 (PHQ-9) prognostic index (PI) as a predictor of post-treatment reliable and clinically significant improvement: Estimation sample.
Figure B2. Patient Health Questionnaire–9 (PHQ-9) prognostic index (PI) as a predictor of post-treatment reliable and clinically significant improvement: Validation sample.
Figure B3. Generalized Anxiety Disorder–7 scale (GAD-7) prognostic index (PI) as a predictor of post-treatment reliable and clinically significant improvement: Estimation sample.
Figure B4. Generalized Anxiety Disorder–7 scale (GAD-7) prognostic index (PI) as a predictor of post-treatment reliable and clinically significant improvement: Validation sample.
Figure B1. Patient Health Questionnaire–9 (PHQ-9) prognostic index (PI) as a predictor of post-treatment reliable and clinically significant improvement: Estimation sample.
Figure B2. Patient Health Questionnaire–9 (PHQ-9) prognostic index (PI) as a predictor of post-treatment reliable and clinically significant improvement: Validation sample.
Figure B3. Generalized Anxiety Disorder–7 scale (GAD-7) prognostic index (PI) as a predictor of post-treatment reliable and clinically significant improvement: Estimation sample.
Figure B4. Generalized Anxiety Disorder–7 scale (GAD-7) prognostic index (PI) as a predictor of post-treatment reliable and clinically significant improvement: Validation sample. APPENDIX C: Logistic Regression Model Predicting (High-Intensity Treatment vs. Low-Intensity Treatment) Group Membership
Variables in the Equation
Variables in the EquationSubmitted: November 14, 2016 Revised: May 16, 2017 Accepted: May 29, 2017
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Source: Journal of Consulting and Clinical Psychology. Vol. 85. (9), Sep, 2017 pp. 835-853)
Accession Number: 2017-36111-001
Digital Object Identifier: 10.1037/ccp0000231
Record: 10- Title:
- Childhood trauma and personality disorder criterion counts: A co-twin control analysis.
- Authors:
- Berenz, Erin C.. Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, US, ecberenz@vcu.edu
Amstadter, Ananda B.. Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, US
Aggen, Steven H.. Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, US
Knudsen, Gun Peggy. Division of Mental Health, Norwegian Institute of Public Health, Oslo, Norway
Reichborn-Kjennerud, Ted. Division of Mental Health, Norwegian Institute of Public Health, Oslo, Norway
Gardner, Charles O.. Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, US
Kendler, Kenneth S.. Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, US - Address:
- Berenz, Erin C., Virginia Institute for Psychiatric and Behavioral Genetics, VA Commonwealth University, Department of Psychiatry, 800 E. Leigh Street, P.O. Box 980126, Richmond, VA, US, 23298-0126, ecberenz@vcu.edu
- Source:
- Journal of Abnormal Psychology, Vol 122(4), Nov, 2013. pp. 1070-1076.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 7
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- co-twin control analysis, personality disorders, stress, trauma, twin study, family factors, genetic factors, environmental factors
- Abstract:
- Correlational studies consistently report relationships between childhood trauma (CT) and most personality disorder (PD) criteria and diagnoses. However, it is not clear whether CT is directly related to PDs or whether common familial factors (i.e., shared environment and/or genetic factors) better account for that relationship. The current study used a cotwin control design to examine support for a direct effect of CT on PD criterion counts. Participants were from the Norwegian Twin Registry (N = 2,780), including a subset (n = 898) of twin pairs (449 pairs, 45% monozygotic [MZ]) discordant for CT meeting DSM–IV Posttraumatic Stress Disorder Criterion A. All participants completed the Norwegian version of the Structured Interview for DSM–IV Personality. Significant associations between CT and all PD criterion counts were detected in the general sample; however, the magnitude of observed effects was small, with CT accounting for no more than approximately 1% of variance in PD criterion counts. A significant, yet modest, interactive effect was detected for sex and CT on Schizoid and Schizotypal PD criterion counts, with CT being related to these disorders among women but not men. After common familial factors were accounted for in the discordant twin sample, CT was significantly related to Borderline and Antisocial PD criterion counts, but no other disorders; however, the magnitude of observed effects was quite modest (r2 = .006 for both outcomes), indicating that the small effect observed in the full sample is likely better accounted for by common genetic and/or environmental factors. CT does not appear to be a key factor in PD etiology. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Early Experience; *Etiology; *Personality Disorders; *Trauma; Environment; Genetics; Twins
- Medical Subject Headings (MeSH):
- Adult; Case-Control Studies; Child; Child Abuse; Diseases in Twins; Educational Status; Female; Humans; Life Change Events; Male; Norway; Personality Disorders; Regression Analysis; Sex Factors; Wounds and Injuries; Young Adult
- PsycINFO Classification:
- Personality Disorders (3217)
- Population:
- Human
Male
Female - Location:
- Norway
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Structured Interview for DSM–IV Personality
Structured Interview for DSM–IV Personality: Norwegian version
Munich-Composite International Diagnostic Interview: Norwegian version
Munich Composite International Diagnostic Interview - Grant Sponsorship:
- Sponsor: National Institutes of Health
Grant Number: MH-068643
Recipients: No recipient indicated
Sponsor: Norwegian Research Council, Norway
Recipients: No recipient indicated
Sponsor: Norwegian Foundation for Health and Rehabilitation, Norway
Recipients: No recipient indicated
Sponsor: Norwegian Council for Mental Health, Norway
Recipients: No recipient indicated
Sponsor: European Commission, Europe
Grant Number: QLG2-CT-2002-01254
Other Details: Under the program “Quality of Life and Management of the Living Resources” of the Fifth Framework Program.
Recipients: No recipient indicated - Methodology:
- Empirical Study; Interview; Quantitative Study; Twin Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Jul 25, 2013; Revised: Jul 23, 2013; First Submitted: Sep 16, 2012
- Release Date:
- 20131223
- Correction Date:
- 20150216
- Copyright:
- American Psychological Association. 2013
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0034238
- PMID:
- 24364608
- Accession Number:
- 2013-44247-012
- Number of Citations in Source:
- 41
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-44247-012&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-44247-012&site=ehost-live">Childhood trauma and personality disorder criterion counts: A co-twin control analysis.</A>
- Database:
- PsycINFO
Childhood Trauma and Personality Disorder Criterion Counts: A Co-twin Control Analysis
By: Erin C. Berenz
Department of Psychiatry, Virginia Institute of Psychiatric and Behavioral Genetics, Virginia Commonwealth University;
Ananda B. Amstadter
Department of Psychiatry, Virginia Institute of Psychiatric and Behavioral Genetics, Virginia Commonwealth University
Steven H. Aggen
Department of Psychiatry, Virginia Institute of Psychiatric and Behavioral Genetics, Virginia Commonwealth University
Gun Peggy Knudsen
Division of Mental Health, Norwegian Institute of Public Health, Oslo, Norway
Ted Reichborn-Kjennerud
Division of Mental Health, Norwegian Institute of Public Health, Oslo, Norway and The Institute of Psychiatry, University of Oslo, Norway
Charles O. Gardner
Department of Psychiatry, Virginia Institute of Psychiatric and Behavioral Genetics, Virginia Commonwealth University
Kenneth S. Kendler
Department of Psychiatry, Virginia Institute of Psychiatric and Behavioral Genetics and Department of Human and Molecular Genetics, Virginia Commonwealth University
Acknowledgement: Supported in part by NIH Grant MH-068643 and grants from the Norwegian Research Council, the Norwegian Foundation for Health and Rehabilitation, the Norwegian Council for Mental Health, and the European Commission under the program “Quality of Life and Management of the Living Resources” of the Fifth Framework Program (QLG2-CT-2002-01254).
Stressful and potentially traumatic life events have been implicated in theories of psychopathology etiology and maintenance (Cicchetti & Cohen, 1997; Linehan, 1993). Exposure to potentially traumatic events during childhood has been related to a number of personality disorder (PD) diagnoses and symptoms (Battle et al., 2004), with Borderline PD receiving the most empirical attention. For example, individuals with clinical or subclinical Borderline PD symptoms are more likely to endorse having experienced childhood abuse compared to nonclinical controls (Bandelow et al., 2005; Laporte, Paris, Guttman, & Russell, 2011; Sansone, Hahn, Dittoe, & Wiederman, 2011). Additionally, etiologic theories of Borderline PD highlight the importance of the role of childhood trauma (CT; Linehan, 1993).
Associations between CT and other PDs also have been documented. For example, childhood maltreatment and abuse are related to Schizotypal PD symptoms (Berenbaum, Thompson, Milanek, Boden, & Bredemeier, 2008; Powers, Thomas, Ressler, & Bradley, 2011), specifically paranoia and unusual perceptual experiences (Steel, Marzillier, Fearon, & Ruddle, 2009). Childhood abuse and witnessing domestic violence have been associated with greater self-reported antisocial behaviors in adolescence (Sousa et al., 2011) and Antisocial PD symptoms in adulthood (Bierer et al., 2003). Other studies have reported broad associations between childhood abuse and the majority of categories of PD symptoms (Battle et al., 2004; Tyrka, Wyche, Kelly, Price, & Carpenter, 2009), even when participants were selected on the basis of having no history of Axis I psychopathology (Grover et al., 2007). Furthermore, childhood abuse and maltreatment are prospectively related to a range of PD symptoms and diagnoses (Cohen, Crawford, Johnson, & Kasen, 2005; Johnson, Cohen, Brown, Smailes, & Bernstein, 1999).
It is tempting to assume that the observed associations between CT and PDs are causal. Although associations have been well documented, a direct effect of CT on PDs has not been established. An alternative explanation is that common mechanisms explain significant covariation between CT and features of PDs (Chapman, Leung, & Lynch, 2008; New et al., 2009). The observed relationship between CT and PDs could also result from common environmental factors (e.g., stressful family environments) and/or shared genetic factors that predispose to both (Button, Scourfield, Martin, Purcell, & McGuffin, 2005; McGuigan & Pratt, 2001; Sartor et al., 2012). Indeed, likelihood of exposure to traumatic events is moderately influenced by genetic factors (Kendler & Baker, 2007), and it is possible that some of these same factors play a role in the development of PDs. Unique environmental influences may also play a role in the CT−PD relationship (e.g., impact of parental reactions on the trauma-exposed child; Nugent, Ostrowski, Christopher, & Delahanty, 2007). Individuals with PDs compared to those without PDs may also be more likely to report a history of CT because of greater negative emotionality, which may bias retrospective reporting (Hardt & Rutter, 2004).
Genetically informative samples consisting of twins who are discordant for CT may provide some insight into the role of CT in PDs, given that shared genetic and environmental factors may be accounted for statistically (Kendler & Campbell, 2009). For example, as traumatic events have been shown to correlate with multiple family background risk factors that are shared by twins (e.g., interpersonal loss, family discord, economic adversity), these factors are statistically controlled for in this model. Without sufficient control in epidemiological samples, which necessitate the measurement of all confounding factors, some of which are unknown, the clustering of traumatic events would likely serve to overestimate the association between CT and PDs. In the one study to our knowledge that addresses trauma and PDs using this design, Bornovalova and colleagues (2013) found that the relationship between childhood abuse and adult Borderline PD traits was likely noncausal and better accounted for by genetic factors. However, this study utilized a questionnaire to assess Borderline traits and did not assess a full range of CT events or PD categories. In fact, the heterogeneity of the assessment and definition of CT in the literature more broadly is problematic. Most notably, many studies fail to assess DSM–IV PTSD Criterion A for trauma exposure (Battle et al., 2004), resulting in a variable that could be assessing stressful life events or negative aspects of the family environment more generally. Past research also focuses largely on child maltreatment and abuse, without assessment or inclusion of other forms of CT that fall within the scope of DSM–IV Criterion A events (Grover et al., 2007). The current investigation sought to address several outstanding limitations of the existing literature by utilizing a genetically informed sample, employing a conservative definition of CT (i.e., DSM–IV PTSD Criterion A), allowing for inclusion of a broad range of CT event types, and using a clinical interview to assess CT and PD criterion counts.
The first aim of the current study was to detect and quantify an association between CT and PD criterion counts in a large sample of adult Norwegian twins obtained from the Norwegian Twin Registry (NTR). It was hypothesized that CT would evidence significant associations with PD criterion counts, above and beyond the effects of age, education level, and participant sex. Given that past studies consistently evidence sex differences in PD prevalence and expression (Torgersen, Kringlen, & Cramer, 2001; Verona, Sprague, & Javdani, 2012), we also evaluated an interaction between CT and participant sex in relation to PD criterion counts. The second aim of the study was to clarify the nature of an association between CT and PD criterion counts in a sample of twins discordant for CT. Specifically, we aimed to evaluate whether CT exerted a direct (i.e., potentially causal) or indirect (i.e., better accounted for by shared environmental and/or genetic factors) effect on PD criterion counts.
Method Sample and Assessment Method
The Norwegian National Medical Birth Registry, established on January 1, 1967, receives mandatory notification of all live births. The NTR identified and recruited twins from the registry, with participants completing questionnaire studies in 1992 (including twins born between 1967 and 1974) and 1998 (including twins born between 1967 and 1979). Of the 6,442 eligible twins that agreed to be contacted again after the second questionnaire, approximately 44% (2,794 twins) participated in an interview study initiated in 1999 (Tambs et al., 2009). This sample also included 68 twin pairs who had not completed the second questionnaire study, but were still recruited (due to technical problems). Data for the current investigation included all participants who completed the interview study and had complete data on PD criterion counts and CT (N = 2,780). This included a subset (n = 616) of twin pairs (46% monozygotic [MZ]) that were discordant for CT. Participants in the general sample (63.5% women) had a mean age of 28.2 (SD = 3.9) at the time of the interview and reported approximately 14.9 years of education (SD = 2.6).
Approval was received from The Norwegian Data Inspectorate and the Regional Ethical Committee approved the study. All participants provided written informed consent. Interviewers were primarily senior clinical psychology graduate students at the end of their 6-year training course (including at least 6 months of clinical practice; 75%) with the remainder (25%) being experienced psychiatric nurses, with the exception of two medical students. The interview training, conducted by one psychiatrist and two psychologists, consisted of a formal presentation on personality disorders, in-class demonstrations of the interview, multiple supervised role plays and test interviews, and group discussion of possible problems and scoring issues. The interviews, mostly face-to-face, were carried out between June, 1999 and May, 2004. For practical reasons, 231 interviews (8.3%) were done by phone. A different interviewer interviewed each twin in a pair.
Assessment of PDs
A Norwegian version of the Structured Interview for DSM–IV Personality (SIDP-IV; Pfohl, Blum, & Zimmerman, 1995), a comprehensive semistructured diagnostic interview, was used to assess all 10 DSM–IV PDs. The SIDP-IV has been successfully used in previous large-scale studies in Norway (Helgeland, Kjelsberg, & Torgersen, 2005; Torgersen et al., 2001). The SIDP-IV contains nonpejorative questions organized into topical sections rather than by individual PD to improve interview flow, and uses the “5-year rule,” meaning that behaviors, cognitions, and feelings that predominated for most of the past 5 years are judged to be representative of an individual’s personality. Each DSM–IV criterion is scored on a 4-point scale (0 = absent, 1 = subthreshold, 2 = present, or 3 = strongly present). Only the A criterion was assessed for Antisocial PD, given the 5-year assessment timeframe (i.e., Criterion C—presence of conduct disorder prior to age 15—was not assessed).
Given the low base rate of PDs, ordinal symptom counts were created that reflect the number of positively endorsed criteria for each disorder. Results from multiple threshold tests of these 10 PDs indicate that the four response options scored as successive integers represent increasing levels of “severity” on a single continuum of liability (Reichborn-Kjennerud et al., 2007; Torgersen et al., 2008). Because few individuals endorsed (scored 2 or greater) most of the criteria for individual PDs, high criterion counts were infrequent. These low frequency, high symptom counts were collapsed so that variation for all PDs was represented as six ordinal categories. This approach has been successfully utilized in past research using the current sample (Reichborn-Kjennerud et al., 2007). Previous studies using the current data have reported high interrater reliability (range of intraclass correlations for endorsed criterion counts = .81−.96) for the assessed PDs obtained by two raters (one psychologist and one psychiatrist interview trainer) scoring 70 audiotaped interviews (Kendler et al., 2008).
Assessment of CT
A Norwegian computerized version of the Munich-Composite International Diagnostic Interview (M-CIDI; (Wittchen & Pfister, 1997), a comprehensive structured diagnostic interview assessing Axis I diagnoses, was administered. The M-CIDI has good test−retest and interrater reliability (Wittchen, 1994; Wittchen, Lachner, Wunderlich, & Pfister, 1998). In the PTSD module of the M-CIDI, participants were asked if they had personally experienced or witnessed any of the following traumas: 1) a terrible experience at war, 2) serious physical threat (with a weapon), 3) rape, 4) sexual abuse as a child, 5) a natural catastrophe, 6) a serious accident, 7) being imprisoned, taken hostage, or kidnapped, or 8) another event. We defined CT as an event occurring before the age of 17 that met DSM–IV PTSD Criteria A1 (i.e., “the person experienced, witnessed, or was confronted with an event or events that involved actual or threatened death or serious injury, or a threat to the physical integrity of self or others) and A2 (i.e., “the person’s response involved intense fear, helplessness, or horror”; American Psychiatric Association, 1994). Approximately 17% (n = 467) of the total sample met these criteria.
Individuals’ worst CT were: 35.0% childhood sexual assault, 15.8% rape, 13.1% an accident, 12.6% an “other” traumatic event, 10.9% physical threat to oneself, 10.9% witnessing a traumatic event, 1.1% a natural disaster, and 0.5% being held hostage.
Data Analytic Plan
Analyses for the current study were conducted in SAS. First, the association between CT and PD criterion counts was examined in the general sample. A series of linear regression models was conducted, with age, education level, participant sex (1 = male, 2 = female), and CT (1 = no CT, 2 = CT) entered in level 1. To examine potential sex differences in the relationship between CT and PD criterion counts, the interaction of participant sex and CT was entered at level 2. A square root transformation was conducted for all PD criterion counts prior to inclusion in the regression models.
Second, a series of fixed effects regressions was conducted to examine CT−PD criterion count relationships among the subset of twin pairs discordant for CT, with twin pair serving as the fixed between-groups factor. This method allows for statistical control of unobserved between-family variation (e.g., genetic factors, family stressors, parenting style, socioeconomic factors, etc.). The relationships between CT and PD criterion counts were compared in the general and discordant twin samples. Based on such comparison, one may determine whether the observed relationship between CT and PD criterion counts in the general sample is likely direct or indirect (i.e., better accounted for by familial factors). Specifically, if the magnitude of the relationship between CT and PD criterion counts were comparable in the general and discordant twin samples, the effect is likely to be direct. If the relationship is significantly lesser in magnitude or nonexistent in the discordant twin sample, the effect is likely to be indirect, or accounted for by shared familial factors. The current investigation was not sufficiently powered to examine discordant MZ and DZ twin pairs separately, which would allow for speculation regarding whether shared environmental or genetic factors were responsible for observed indirect effects. For a more comprehensive overview of the cotwin control design, see K.S. Kendler et al.(1993). A total of 20 regression models (10 in the full sample, 10 in the discordant twin subsample) were conducted. Bonferroni correction for multiple testing indicated statistical significance at p = .003.
Results Descriptive Statistics and Zero-Order Correlations (General Sample)
See Table 1. Sex was significantly related to CT, with men being more likely to endorse a CT. Men also were more likely to endorse a greater number of criteria for Narcissistic, Antisocial, and Obsessive-Compulsive PDs, while women were more likely to endorse criteria for Schizotypal, Histrionic, Borderline, and Dependent PDs. Age and years of education were significantly related to various PD criterion counts with no particular pattern being observed. CT was significantly, yet modestly, related to a greater number of criteria for all PDs, with the exception of Avoidant PD.
Descriptive Statistics and Zero-Order Correlations in the Total Sample
Trauma Exposure and PD Criterion Counts
See Table 2 for regression statistics for the main effect of CT on PD criterion counts in the full and discordant twin samples. CT was significantly related to all PD criterion counts in the full sample, after covarying for sex, age, and education level, with the exception of Schizoid, Avoidant, and Dependent PDs. The greatest effects were observed for Borderline (r2 = .013), Obsessive-Compulsive (r2 = .008), Schizotypal (r2 = .007), and Antisocial PDs (r2 = .007; see Figure 1 for a comparison of effect sizes for all PD criterion counts). An interaction between CT and sex was statistically significant for Schizoid (β = .25, t = 2.95, p = .003) and Schizotypal PDs (β = .26, t = 3.16, p = .002). CT was significantly, yet modestly, related to Schizoid and Schizotypal PD criterion counts among women (β = .10, t = 4.10, sr2 = .01, p < .001; and β = .13, t = 5.31, sr2 = .02, p < .001, respectively) but not men (β = −.01, t = −.44, p = .661; and β = .01, t = .42, p = .678, respectively).
Childhood Trauma Predicting Personality Disorder Criterion Counts
Figure 1. Magnitude of relationship between childhood trauma and personality disorder criterion counts. Note: * p < .003, indicating statistical significance after Bonferroni correction for multiple testing within the indicated sample; analyses in the full sample represent the effect of childhood trauma on criterion counts above and beyond the variance accounted for by participant age, education level, and sex.
Results of the fixed effects regressions indicated that after accounting for family and genetic factors in the discordant twin sample, the relationship between CT and PD criterion counts was quite modest (see Table 2). Overall, the magnitude of the effect of CT on PD criterion counts was quite small prior to accounting for familial liability, not accounting for more than 1% of variance for any given disorder, and upon controlling for familial factors, the relationship between CT and PD criterion counts was essentially nonexistent (see Figure 1 for a comparison of effect sizes in the two samples). The potential exceptions to this pattern are for Borderline and Antisocial PD criterion counts, for which CT appears to account for a slight proportion of unique variation (<1%) beyond shared familial liability.
DiscussionOur first aim was to examine associations between CT and PD criterion counts in the general sample. Consistent with past work, CT was significantly associated with the majority of PD criterion counts, after accounting for sex, age, and education (Battle et al., 2004; Grover et al., 2007; Tyrka et al., 2009). Significant associations were not detected for Schizoid, Avoidant, or Dependent PDs. The strongest associations were detected for Borderline, Obsessive-Compulsive, Schizotypal, and Antisocial PD criterion counts; however, CT accounted for less than 2% of variance in these disorders, even without the inclusion of rigorous covariates (e.g., life stressors, parenting style, etc.). In contrast, trait-level neuroticism accounts for approximately 45% of variance in Borderline PD traits prior to accounting for shared genetic factors (Distel et al., 2009).
The relationship between CT and Schizoid and Schizotypal PD criterion counts varied by sex, with a significant trauma−PD association being detected among women but not men. It is possible that CT is associated with factors promoting a decreased capacity for or interest in close relationships among women. However, the magnitude of effects was consistent with the pattern observed in the full sample, with CT accounting for no more than 2% of variance in these disorders. Prior to controlling for the role of familial factors, CT appears to exert quite modest influence on PD criterion counts.
Second, we sought to explain the nature of the observed effects through the use of a cotwin control design, which relies on comparing the magnitude of effects in the general sample with that observed in twins discordant for CT. The relationships between CT and Antisocial and Narcissistic PDs were comparable in the two samples. It is possible that the very small (i.e., r2 < .01) effect of CT is causal in nature for these disorders. CT also was significantly related to Borderline PD criterion counts in the discordant twin sample; although, the magnitude of the effect in the discordant twins was 50% of that observed in the full sample, suggesting that a substantial portion of the relationship is likely better accounted for by familial factors rather than a direct effect of CT. The role of CT in the remaining PD criterion counts was essentially nonexistent in the discordant twin sample. It does not appear that CT plays a substantial role in the development of PD symptoms.
These findings are at odds with existing theories and clinical intuition regarding PD etiology, particularly in the case of Borderline PD. For example, one review paper on CT in Borderline PD concluded that, “the evidence suggests that childhood trauma should be included in a multifactorial model of BPD” (Ball & Links, 2009). It is worth noting that the studies reviewed by the authors were purely correlational and did not account for the role of familial factors. Marsha Linehan cites CT as a “prototypic invalidating experience” contributing to a biosocial model of Borderline PD etiology, with an entire stage of treatment being devoted to addressing CT and reducing trauma-related behaviors in Dialectical Behavior Therapy (Linehan, 1993). Finally, there has been empirical interest in discovering a biological link between CT and PDs. It has been suggested that CT may lead to neurobiological alterations (e.g., disruption of serotonin function), which in turn may lead to emotional and behavioral deficits in PDs (Lee, 2006). The current findings would suggest that focus on CT as a key risk factor in PDs may not be particularly fruitful. Indeed, this study replicates and extends the only other investigation to our knowledge that addresses CT and PDs using a discordant twin design, in which little to no direct relationship between CT and Borderline PD symptoms was detected (Bornovalova et al., 2013). Investigation of traumatic events in the etiology of Axis I psychopathology using genetically informed designs is needed.
The current project has a number of limitations. First, the base rate of high criterion counts in the current sample was quite low, preventing analysis at the diagnostic level. Future studies utilizing large samples and diverse methodologies are needed. Second, this study was underpowered to examine trauma type or severity in relation to PD criterion counts. Similarly, this study did not have data on familial response to CT. Parental response to trauma may influence the trauma-exposed child’s unique environment. For example, parental posttraumatic stress disorder symptoms and general parental distress may exert unique influence on children’s reactions to trauma (Nugent et al., 2007). Future studies investigating trauma characteristics and responses of the individual and his or her family to CT may be useful. Third, the ethnic and age composition of the current sample is relatively homogenous. Fourth, this study relied on retrospective reporting of CT. However, one would expect this reporting method to inflate the relationship between CT and PD symptoms; therefore, it is possible that the very modest effects detected in the current sample are upwardly biased by recall effects. Fifth, this study did not include a social desirability measure, which may be useful for future investigations in order to account for potential reporting bias. Sixth, this study was underpowered to analyze MZ and DZ twins separately, which would have provided additional information on whether genetic or shared environmental factors accounted for the relationship between CT and PD criterion counts. Finally, this study did not assess for a history of conduct disorder symptoms as related to Antisocial PD. Despite these limitations, the current study provides novel data on the relationship between CT and PD criterion counts.
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Submitted: September 16, 2012 Revised: July 23, 2013 Accepted: July 25, 2013
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Source: Journal of Abnormal Psychology. Vol. 122. (4), Nov, 2013 pp. 1070-1076)
Accession Number: 2013-44247-012
Digital Object Identifier: 10.1037/a0034238
Record: 11- Title:
- Chronic sleep disturbances and borderline personality disorder symptoms.
- Authors:
- Selby, Edward A.. Department of Psychology, Rutgers University, Piscataway, NJ, US, edward.selby@rutgers.edu
- Address:
- Selby, Edward A., Department of Psychology, Rutgers University, Tillett Hall, 53 Avenue East, Piscataway, NJ, US, 08854, edward.selby@rutgers.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 81(5), Oct, 2013. pp. 941-947.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 7
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- borderline personality disorder, emotion dysregulation, fatigue, insomnia, sleep, chronic sleep disturbances
- Abstract:
- Objective: Few studies have examined the experience of chronic sleep disturbances in those with borderline personality disorder (BPD), and further establishing this association may be pertinent to enhancing current treatments, given the relevance of sleep to emotion regulation and stress management. Method: Data were analyzed (N = 5,692) from Part II of the National Comorbidity Survey–Replication (NCS-R) sample (Kessler & Merikangas, 2004), which assessed personality disorders and sleep problems. Rates of chronic sleep disturbances (difficulty initiating sleep, difficulty maintaining sleep, and waking earlier than desired), as well as the consequences of poor sleep, were examined. Indices for BPD diagnosis and symptoms were used in logistic and linear regression analyses to predict sleep and associated problems after accounting for chronic health problems, Axis I comorbidity, suicidal ideation over the last year, and key sociodemographic variables. Results: BPD was significantly associated with all 3 chronic sleep problems assessed, as well as with the consequences of poor sleep. The magnitude of the association between BPD and sleep problems was comparable to that for Axis I disorders traditionally associated with sleep problems. BPD symptoms interacted with chronic sleep problems to predict elevated social/emotional, cognitive, and self-care impairment. Conclusions: Sleep disturbances are consistently associated with BPD symptoms, as are the daytime consequences of poor sleep. There may also be a synergistic effect where BPD symptoms are aggravated by poor sleep and lead to higher levels of functional impairment. Sleep in patients with BPD should be routinely assessed, and ameliorating chronic sleep problems may enhance treatment by improving emotion regulation and implementation of therapeutic skills. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Borderline Personality Disorder; *Sleep Disorders; *Symptoms; Emotional Regulation; Fatigue; Insomnia
- Medical Subject Headings (MeSH):
- Adult; Borderline Personality Disorder; Chronic Disease; Comorbidity; Female; Health Surveys; Humans; Male; Single-Blind Method; Sleep Wake Disorders; United States
- PsycINFO Classification:
- Psychological & Physical Disorders (3200)
- Population:
- Human
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- International Personality Disorder Examination
World Health Organization Disability Assessment Schedule II
Composite International Diagnostic Interview DOI: 10.1037/t02121-000 - Grant Sponsorship:
- Sponsor: Brain and Behavior Research Foundation
Recipients: No recipient indicated
Sponsor: Families for Borderline Personality Disorder Research
Other Details: early investigator grant
Recipients: Selby, Edward A. (Prin Inv)
Sponsor: National Institute of Mental Health
Grant Number: U01-MH60220
Recipients: No recipient indicated
Sponsor: National Institute on Drug Abuse
Recipients: No recipient indicated
Sponsor: Substance Abuse and Mental Health Services Administration
Recipients: No recipient indicated
Sponsor: Robert Wood Foundation
Grant Number: 044708
Recipients: No recipient indicated
Sponsor: John W. Alden Trust
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Jun 3, 2013; Accepted: Apr 29, 2013; Revised: Feb 15, 2013; First Submitted: Sep 20, 2012
- Release Date:
- 20130603
- Correction Date:
- 20160616
- Copyright:
- American Psychological Association. 2013
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0033201
- PMID:
- 23731205
- Accession Number:
- 2013-19431-001
- Number of Citations in Source:
- 25
- Persistent link to this record (Permalink):
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- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-19431-001&site=ehost-live">Chronic sleep disturbances and borderline personality disorder symptoms.</A>
- Database:
- PsycINFO
Chronic Sleep Disturbances and Borderline Personality Disorder Symptoms / BRIEF REPORT
By: Edward A. Selby
Rutgers University;
Acknowledgement: Support for this project was provided, in part, by the Brain and Behavior Research Foundation and the Families for Borderline Personality Disorder Research with an early investigator grant (Edward A. Selby, principal investigator). The National Comorbidity Survey–Replication (NCS-R) was supported by National Institute of Mental Health Grant U01-MH60220, with supplemental support from the National Institute on Drug Abuse, the Substance Abuse and Mental Health Services Administration, the Robert Wood Foundation (Grant 044708), and the John W. Alden Trust. The views and opinions expressed in this report are those of the author and should not be construed to represent the views of any sponsoring organizations, agencies, or the U.S. government. A complete list of NCS publications and the full text of all NCS-R instruments can be found at http://www.hcp.med.harvard.edu/ncs. The NCS-R is carried out in conjunction with the World Health Organization World Mental Health (WMH) Survey Initiative.
Although not traditionally thought of as a disorder associated with sleep disturbances, there is growing evidence that those with borderline personality disorder (BPD) experience a variety of problems with sleep, including increased sleep onset latency and low sleep efficiency during polysomnography assessments (Bastien, Guimond, St-Jean, & Lemelin, 2008), abnormal sleep architecture (Battaglia, Strambi, Bertella, Bajo, & Bellodi, 1999), and nightmares (Asaad, Okasha, & Okasha, 2002; Selby, Ribeiro, & Joiner, in press). Sleep problems are clinically pertinent to BPD, as they are linked to functional impairment (Roth et al., 2006) and emotion dysregulation (Zohar, Tzischinsky, Epstein, & Lavie, 2005). To date, minimal research has systematically examined sleep disturbances in BPD, particularly chronic sleep problems. Chronic sleep problems involve difficulty sleeping most nights for an extended period of time (often lasting weeks to months), as opposed to acute sleep problems, which may last for a few days or arise intermittently, and can lead to major problems in daily functioning (Simon & Von Korff, 1997). Research is also lacking on BPD and the daytime consequences of chronic sleep problems, such as excessive daytime sleepiness, poor sleep-related fatigue, and difficulties engaging in activities due to poor sleep. Importantly, BPD may increase vulnerability to sleep problems, due to issues such as emotion dysregulation, and poor sleep may result in elevated daytime functional impairment.
Improving our understanding of sleep disturbances in BPD is also relevant to improving our interventions. At present, dialectical behavior therapy (DBT; Linehan, 1993) is the only psychotherapy for BPD that specifically addresses sleep problems. Improving the sleep of patients with BPD may aid in improving their ability to manage stressful situations, employ coping skills, and improve overall levels of energy and positive affect. In turn, improving the ability to manage stress may further reduce sleep problems (Carlson & Garland, 2005). Those with BPD also frequently experience suicidal ideation, the experience of which has itself also been linked to sleep problems (Sjöström, Wærn, & Hetta, 2007; Wojnar et al., 2009). This makes it important to determine if the sleep problems of those with BPD also occur beyond the context of suicidal ideation.
Previous studies on sleep and BPD have involved small samples, often during acute polysomnography studies, and none to date have examined BPD and chronic sleep problems in a large epidemiological sample. One advantage to using such a sample is reduced treatment-seeking bias and increased understanding of how these issues affect people in the community at-large. Another advantage is the ability to control for Axis I disorders that are intertwined with acute and chronic sleep problems (e.g., depression, anxiety disorders)—an important issue given the role of comorbidity in BPD (Lenzenweger, Lane, Loranger, & Kessler, 2007). The purpose of the present study was to examine chronic sleep disturbances, poor sleep-related consequences, and social/emotional and cognitive impairment as a function of poor sleep in those exhibiting BPD symptoms with the National Comorbidity Survey–Replication (NCS-R; Kessler & Merikangas, 2004).
Method Sample and Diagnostic Assessment
The NCS-R was a nationally representative, institutional review board–approved survey of adults age 18 and older designed to involve multistage clustered area probability sampling and conducted between 2001 and 2003. Overall, the survey consisted of 9,282 respondents, with an overall response rate of 70.9%. All respondents in the NCS-R completed the Part I diagnostic interview, which consisted of the World Health Organization (WHO) Composite International Diagnostic Interview (CIDI 3.0; Kessler & Üstün, 2004) and included diagnostic information on anxiety disorders, mood disorders, and substance use disorders. Blinded clinical reappraisals of a probability subsample of the NCS-R indicated good concordant validity between the Axis I diagnosis (Kessler et al., 2005) according to the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM–IV;American Psychiatric Association, 1994) and the CIDI diagnosis. In addition to completing assessment the CIDI, a probability subsample of respondents (N = 5,692), with and without DSM–IV diagnoses, also received the NCS-R Part II interview, which also assessed personality disorders and sleep problems; only NCS-R Part II data were used in this study.
BPD Symptoms
Respondents completed eight questions taken from the International Personality Disorder Examination (IPDE) screening questionnaire (Loranger et al., 1994) designed to measure BPD symptoms. These items have been used extensively in examining personality disorders (e.g., Lenzenweger, 1999; Lenzenweger et al., 2007) and have been found to significantly predict personality disorder diagnoses when the IPDE was clinician-administered (Loranger, 1999; Loranger et al., 1994). Furthermore, the more IPDE items were endorsed, the higher was the probability that a personality disorder diagnosis was obtained using structured clinical interviews (Lenzenweger, Loranger, Korfine, & Neff, 1997). Items were rated with a dichotomous yes (1) or no (0) answer, and all were summed to create a continuous measure of BPD (α = .74). Importantly, clinical reappraisal interviews were previously conducted by phone on a probability subsample of 214 respondents from Part II of the NCS-R and screened positive for personality disorder symptoms, and clinical reassessments with the IPDE were highly correlated (r > .90) with these items (Lenzenweger et al., 2007). Of note, because the NCS-R included a separate suicidal behavior assessment, the IPDE BPD question on suicidal and self-injurious behavior was not included. As a result of this missing criterion, three indices of BPD were generated for use in this study: (1) a dichotomous measure for those endorsing 5+ diagnostic criteria for BPD (to obtain odds ratios), (2) a continuous measure of BPD symptoms, and (3) a dichotomous BPD diagnosis (5+ symptoms endorsed) including occurrence of a lifetime suicide attempt (from the suicide assessment below).
Regarding the distribution of BPD symptoms, Selby (2013) found that the base rates (BRs) of BPD symptoms in this sample, as well as the endorsement of those symptoms by those with BPD (diagnostic sensitivity; DS), were well represented: intense anger (BR = 23%, DS = 73%), affective instability (BR = 30%, DS = 90%), chronic emptiness (BR = 21%, DS = 83%), identity disturbance (BR = 19%, DS = 73%), stress-related paranoia (BR = 17%, DS = 69%), avoiding abandonment (BR = 12%, DS = 60%), impulsivity (BR = 40%, DS = 89%), and unstable relationships (BR = 15%, DS = 60%). Thus, those with BPD in this sample exhibited the full spectrum of BPD symptoms.
Chronic Sleep Problems
Three common sleep problems assessed during Part II of the NCS-R were used (1) delayed sleep onset latency (SOL; “Nearly every night it took you two hours or longer before you could fall asleep”), (2) amount of time spent awake after sleep onset (WASO; “You woke up nearly every night and took an hour or more to get back to sleep”), and (3) waking earlier in the morning than desired (EMA; “You woke up nearly every morning at least two hours earlier than you wanted to”). Each was prefaced as happening for “periods lasting two weeks or longer in the past 12 months.” All were consistent with DSM–IV definitions for the associated sleep problem and have been reported on previously (Roth et al., 2006).
Poor Sleep Consequences
In addition to chronic sleep problems, this study examined the consequences of poor sleep using three assessments: (1) problems “feeling sleepy during the day,” (2) feeling fatigued during the day due to “poor sleep,” and (3) frequency of being “too tired to complete daily activities” as a result of poor sleep. All questions were assessed in the same section as the above questions about chronic sleep problems. The first question, on the experience of daytime sleepiness most days for two or more weeks in the past 12 months, was coded as yes (1) or no (0). The second question, relating to daytime fatigue as a result of poor sleep, and the third question, regarding frequency of being too tired to carry out daily activities—both of which were asked in terms of the frequency of the problem during the worst month in the past year—were rated on 4-point Likert scales. These two questions were originally rated 1 (never), 2 (rarely), 3 (sometimes), and 4 (often). In order to simplify the presentation of results and present odds ratios, these items were recoded to reflect that 4 (often experience of this problem) was recoded as (1), and all other ratings were coded as (0).
Functional Impairment
Six outcomes were utilized from the World Health Organization Disability Assessment Schedule II (WHO-DAS II; Chwastiak & Von Korff, 2003), a 36-item measure for general disability impairment. Respondents reported the severity of each problem over the last 30 days regarding the following: (1) days having been totally unable to work or carry out daily activities (days out of role), (2) days able to carry out normal activities but with reduced workload or inhibited productivity (reduced work quality), (3) difficulty caring for oneself with activities such as hygiene (self-care), (4) difficulties with physical mobility, (5) cognitive impairment (e.g., difficulty remember things), and (6) difficulty with social and emotional role performance (e.g., controlling emotions when around other people). The complex method (Chwastiak & Von Korff, 2003), which involves item response theory, was used to score each scale, and this resulted in a continuous scale range from 0 (No disability) to 100 (Full disability).
Comorbid DSM–IV Disorders
All respondents were rated for Axis I diagnoses with the CIDI, which has good concordance with other structured clinical interviews (Kessler & Üstün, 2004; Kessler et al., 2005). The following disorders, which were present over the last 12 months, were included as covariates in all analyses: major depressive disorder (MDD), dysthymia, manic episode, alcohol and drug dependence, panic disorder (with and without agoraphobia), generalized anxiety disorder (GAD), and posttraumatic stress disorder (PTSD). Each of these disorders has sleep problems as a symptom or has been linked to sleep problems, and their inclusion allowed for benchmarks to compare the association between BPD and sleep problems to. To examine comorbidity, variables were generated to indicate presence (1) or absence (0) of any two or three Axis I disorders.
Suicidality
As a part of the CIDI, all respondents completed a suicide risk assessment that included lifetime history of suicidal behavior as well as suicidal behavior in the last 12 months. Those who had “seriously thought about committing suicide” were coded and used as a covariate in analyses, and the presence of a lifetime suicide attempt was also used to generate one of the BPD indices described above.
Sociodemographic Control Variables
The following variables from the NCS-R were included in all analyses due to their potential impact on sleep problems: age, sex, race–ethnicity, education level, marital status, occupational status, family income level, and number of preschool children living at home. The relationships between covariates and personality disorders and sleep problems has previous been reported on (Lenzenweger et al., 2007; Roth et al., 2006). Also included was the presence or absence of any one of the following chronic health conditions endorsed, due to potential sleep interference: arthritis, neck or back ache, headaches, chronic pain, chronic allergies, stroke, and heart disease.
Data Analytic StrategyLogistic regression analyses were used to examine sleep problems and consequences, with the coefficients and their standard errors exponentiated as odds ratios with 95% confidence intervals. All analyses included medical and sociodemographic control variables, comorbid Axis I disorders, and presence of suicidal ideation in the last year; adjusted odds ratios (AORs) were presented. Finally, the interactions between BPD symptoms and chronic sleep problems in predicting functional impairment were examined with linear regression. Because the NCS-R was a complex sample involving clustering, stratification, and weighting specific to Part II, in order to adjust for potential differences in probability of selection for the sample, data analysis had to account for these procedures. Accordingly, standard errors of the logistic regression and linear regression analyses were adjusted for these sampling and weighting procedures with the COMPLEX function of the MPlus statistical program (Muthén & Muthén, 2008–2010).
Results Prevalence of Sleep Problems and Borderline Personality Disorder
A total of 63% of those meeting diagnostic criteria for BPD (5+ symptoms endorsed) reported having at least one of the sleep problems assessed. The average duration for sleep problems for those with BPD was 19.9 weeks (SD = 21.6), which was significantly more than for those without BPD (M = 8.9, SD = 17.2), F(1, 6590) = 45.4, p < .01, d = 0.60. As seen in Table 1, those with BPD reported significant experiences with delayed SOL (AOR = 1.8, wald = 33.3, p < .01), WASO (AOR = 1.9, wald = 41.3, p < .01), and EMA (AOR = 2.3, wald = 64.8, p < .01). BPD diagnosis was also a significant predictor of having all three chronic sleep problems (27% of those with BPD; wald = 37.75, p < .01, AOR = 2.1). BPD associations with each of the chronic sleep problems were similar in magnitude to those of disorders traditionally linked to sleep problems or involving sleep problems in diagnostic criteria (e.g., GAD, MDD, PTSD).
Multivariate Logistic Regression Analyses Predicting Sleep Problems (N = 5,692)
Borderline Personality Disorder and Poor Sleep-Related Consequences
Approximately 66% of those with BPD reported having at least one consequence over the last 12 months. BPD diagnosis (see Table 2) demonstrated clear and consistent associations, beyond covariates, with sleepiness during the day (AOR = 2.0, wald = 51.2, p < .01), daytime fatigue due to poor sleep (AOR = 2.1, wald = 43.4, p < .01), and being too tired to complete daily activities (AOR = 1.9, wald = 12.8, p < .01). BPD diagnosis was also a significant predictor of having all three poor sleep consequences (6% of those with BPD; wald = 7.5, p < .01, AOR = 2.1). As with chronic sleep problems, BPD diagnosis had AORs similar in magnitude to those of many Axis I disorders traditionally associated with sleep problems.
Multivariate Logistic Regression Analyses Predicting Poor Sleep Consequences (N = 5,692)
Functional Impairment Associated With Poor Sleep and Borderline Personality Symptoms
BPD interacted with sleep problems (see Table 3) to indicate more problems with self-care (β = .08, t = 3.0, p < .01), cognitive impairment (β = .17, t = 7.1, p < .001), and social/emotional impairment (β = .19, t = 7.9, p < .001) after accounting for key covariates. Figure 1 indicates worse impairments for those with more BPD symptoms who exhibited more sleep problems for these areas of functioning. No significant interactions were found for days out of role, role impairment, or decreased physical mobility.
BPD and Chronic Sleep Problems Predicting Functional Impairment
Figure 1. Interaction between BPD symptoms and chronic sleep problems on functional impairment on the World Health Organization Disability Assessment Schedule II. Low and high levels refer to one standard deviation below and above the mean, respectively. BPD = borderline personality disorder (referring to BDP symptoms); SLP = sleep (referring to the number of sleep problems).
DiscussionThe current study found a clear and consistent report of chronic sleep disturbances in those with BPD, even after accounting for key covariates, with many experiencing delayed SOL, increased WASO, and increased EMA most days for at least 2 weeks over the last year. Interestingly, the magnitudes of the AORs for BPD on chronic sleep problems was similar to those for other Axis I disorders often associated with sleep disturbance and with sleep-related criteria (i.e., MDD, GAD). BPD was also significantly associated, due to problems sleeping, with increased daytime sleepiness, fatigue, and feeling too tired to complete daytime activities. Potential reasons for the association between BPD and sleep problems, beyond Axis I contributions, may be that BPD psychopathology increases vulnerability to sleep problems because of emotion dysregulation or rumination (Selby & Joiner, 2009) or because of interpersonal conflicts during the day. Such experiences during the day may translate into difficulty falling asleep or frequent awakenings due to preoccupation with the problems or increased arousal. They may also experience frequent nightmares on days with more emotion dysregulation (Selby et al., in press).
Findings also indicated that when those with BPD have sleep problems they might experience increased difficulties with emotion dysregulation, problems in social relationships or self-care, and memory problems. These interactions suggest that when both problems are present a positive feedback loop may arise where BPD symptoms may contribute to poor sleep and poor sleep aggravates symptoms of BPD. However, there may be differential effects of sleep problems on aggravating BPD symptoms, and some may be more affected than others. For example, despite the finding that many with BPD reported feeling too tired to complete daily activities due to poor sleep, BPD symptoms did not interact with sleep problems to predict number of days out of role or reduced role quality, nor did the interaction predict decreased physical mobility. However, those with elevated BPD symptoms and sleep problems reported more cognitive and social/emotional impairment. These findings indicated that when those with BPD are experiencing sleep problems, they may have increased problems with issues such as emotion dysregulation, social relationships, and remembering things (potentially impacting techniques and skills learned in therapy). Furthermore, a significant interaction was also found for those with BPD and sleep problems in predicting decreased ability for self-care, and reduced self-care also likely worsens problems with emotional, social, and cognitive regulation. Future research should compare those with BPD who do and do not have chronic sleep problems to determine if there are significant group differences in intensity or duration of specific BPD symptoms or if symptoms are exacerbated at a more general level.
Important strengths of the current study to consider include use of a large, nontreatment-seeking sample and controlling for medical, Axis I, and sociodemographic variables associated with sleep problems. One primary limitation involved cross-sectional assessment of sleep problems over the last year (e.g., potential recall bias), and there was some temporal inconsistency between the indices measured over the course of the last year (sleep problems) and the indices measured over the last 30 days (functional impairment). Longitudinal studies are needed to determine if BPD increases vulnerability to sleep problems or if sleep problems simply aggravate BPD symptoms. Further, all indices in this study were self-report, and further studies with sleep diary monitoring and/or polysomnography studies may be needed to replicate and extend these findings and ascertain more precise assessments of chronic sleep problems in BPD. Another limitation was that BPD was assessed with IPDE screening items, and findings should be replicated in samples where BPD is assessed with structured clinical interviews. Finally, other factors may be involved in the BPD association with poor sleep, such as being hypervigilant or worried about sleep problems, poor sleep environment, or sleep state misperceptions.
Clinically, monitoring of sleep problems and associated impairment may be important in therapy, and it may be beneficial to thoroughly cover facets of sleep hygiene as a routine part of therapy for those with BPD. Although some therapies for BPD integrate sleep hygiene to some extent (e.g., the emotion regulation module of DBT; Linehan, 1993), many therapies may overlook this issue. It may be the case that treating sleep will help reduce negative emotion in those with BPD, and such daytime improvements may further improve overall sleep quality. Finally, sleep hygiene alone may not be enough to treat some BPD patients with chronic sleep problems, and some patients may also benefit from additional cognitive behavior therapy for insomnia (Edinger & Means, 2005).
Footnotes 1 Diagnostic efficiency analyses of the IPDE BPD screening items have indicated adequate sensitivity (all >.60), specificity (all >.66), negative predictive power (all >.95), and total predictive value (all >.68); however, all were somewhat low on positive predictive power (range .24–.53), indicating that endorsing only one symptom was not a strong indicator for the presence of a BPD diagnosis and supporting the notion that multiple items needed to be endorsed for a diagnosis (Selby, 2013).
2 The main effects of BPD diagnosis on the six indices of impairment measured by the WHO-DAS II have previously been reported on, with significant effects found for BPD diagnosis on mobility, cognition, days out of role, diminished role quality, and problems with social and emotional functioning (Lenzenweger et al., 2007). However, the current study builds on these findings by examining the interaction between a continuous predictor of BPD symptoms and the number of chronic sleep problems assessed, and in this study the WHO-DAS II scales were left as continuous variables rather than dichotomized as in the previous study.
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Submitted: September 20, 2012 Revised: February 15, 2013 Accepted: April 29, 2013
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Source: Journal of Consulting and Clinical Psychology. Vol. 81. (5), Oct, 2013 pp. 941-947)
Accession Number: 2013-19431-001
Digital Object Identifier: 10.1037/a0033201
Record: 12- Title:
- Cognitive behavioral therapy for adherence and depression (CBT-AD) in HIV-infected injection drug users: A randomized controlled trial.
- Authors:
- Safren, Steven A.. Massachusetts General Hospital, MA, US, ssafren@partners.org
O'Cleirigh, Conall M.. Massachusetts General Hospital, MA, US
Bullis, Jacqueline R.. Harvard Medical School, Boston, MA, US
Otto, Michael W.. Department of Psychology, Boston University, Boston, MA, US
Stein, Michael D.
Pollack, Mark H.. Massachusetts General Hospital, MA, US - Address:
- Safren, Steven A., MGH Behavioral Medicine, One Bowdoin Square, 7th Floor, Boston, MA, US, 02114, ssafren@partners.org
- Source:
- Journal of Consulting and Clinical Psychology, Vol 80(3), Jun, 2012. pp. 404-415.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 12
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- HIV/AIDS, adherence, antiretroviral therapy (ART), depression, substance abuse, cognitive behavioral therapy for adherence and depression, injection drug users
- Abstract:
- Objective: Depression and substance use, the most common comorbidities with HIV, are both associated with poor treatment adherence. Injection drug users comprise a substantial portion of individuals with HIV in the United States and globally. The present study tested cognitive behavioral therapy for adherence and depression (CBT-AD) in patients with HIV and depression in active substance abuse treatment for injection drug use. Method: This is a 2-arm, randomized controlled trial (N = 89) comparing CBT-AD with enhanced treatment as usual (ETAU). Analyses were conducted for two time-frames: (a) baseline to post-treatment and (b) post-treatment to follow-up at 3 and 6 months after intervention discontinuation. Results: At post-treatment, the CBT-AD condition showed significantly greater improvement than ETAU in MEMS (electronic pill cap) based adherence, γslope = 0.8873, t(86) = 2.38, p = .02; dGMA-raw = 0.64, and depression, assessed by blinded assessor: Mongomery-Asberg Depression Rating Scale, F(1, 79) = 6.52, p < .01, d = 0.55; clinical global impression, F(1, 79) = 14.77, p < .001, d = 0.85. After treatment discontinuation, depression gains were maintained, but adherence gains were not. Viral load did not differ across condition; however, the CBT-AD condition had significant improvements in CD4 cell counts over time compared with ETAU, γslope = 2.09, t(76) = 2.20, p = .03, dGMA-raw = 0.60. Conclusions: In patients managing multiple challenges including HIV, depression, substance dependence, and adherence, CBT-AD is a useful way to integrate treatment of depression with an adherence intervention. Continued adherence counseling is likely needed, however, to maintain or augment adherence gains in this population. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Cognitive Behavior Therapy; *Drug Therapy; *HIV; *Major Depression; *Treatment Compliance; AIDS; Antiviral Drugs; Drug Abuse; Intravenous Drug Usage
- Medical Subject Headings (MeSH):
- Aged; Antiretroviral Therapy, Highly Active; Cognitive Therapy; Depressive Disorder; Female; HIV Infections; Humans; Male; Medication Adherence; Middle Aged; Substance Abuse, Intravenous; Substance-Related Disorders; Treatment Outcome
- PsycINFO Classification:
- Health & Mental Health Treatment & Prevention (3300)
- Population:
- Human
Male
Female - Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs)
Aged (65 yrs & older) - Tests & Measures:
- Clinical Global Impression
Beck Depression Inventory DOI: 10.1037/t00741-000
Montgomery-Asberg Depression Rating Scale DOI: 10.1037/t04111-000
Mini International Neuropsychiatric Interview DOI: 10.1037/t18597-000 - Grant Sponsorship:
- Sponsor: National Institute on Drug Abuse
Grant Number: Grant R01DA018603
Recipients: Safren, Steven A. - Conference:
- International Conference on HIV Treatment (IAPAC), 5th, May, 2010, Miami, FL, US
- Conference Notes:
- Portions of this article were presented at the aforementioned conference.
- Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Apr 30, 2012; Accepted: Feb 7, 2012; Revised: Feb 6, 2012; First Submitted: May 9, 2011
- Release Date:
- 20120430
- Correction Date:
- 20130715
- Copyright:
- American Psychological Association. 2012
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0028208
- PMID:
- 22545737
- Accession Number:
- 2012-10794-001
- Number of Citations in Source:
- 56
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-10794-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-10794-001&site=ehost-live">Cognitive behavioral therapy for adherence and depression (CBT-AD) in HIV-infected injection drug users: A randomized controlled trial.</A>
- Database:
- PsycINFO
Cognitive Behavioral Therapy for Adherence and Depression (CBT-AD) in HIV-Infected Injection Drug Users: A Randomized Controlled Trial
By: Steven A. Safren
Massachusetts General Hospital;
Harvard Medical School;
Conall M. O'Cleirigh
Massachusetts General Hospital;
Harvard Medical School
Jacqueline R. Bullis
Harvard Medical School;
Department of Psychology, Boston University
Michael W. Otto
Department of Psychology, Boston University
Michael D. Stein
Butler Hospital and Brown University
Mark H. Pollack
Massachusetts General Hospital;
Harvard Medical School
Acknowledgement: Funding for this project came from National Institute on Drug Abuse Grant R01DA018603 to Steven A. Safren. Clinical Trial Registration: Skills Based Counseling for Adherence and Depression in HIV+ Methadone Patients (NCT00218634; http://clinicaltrials.gov.offcampus.lib.washington.edu/ct2/show/NCT00218634). Portions of this article were presented at the fifth International Conference on HIV Treatment (IAPAC), May 2010, Miami, Florida.
Safren has served as a consultant for Dimangi and ISA Associates on technology-based adherence interventions and receives royalties from various book publishers, including Oxford University Press and Guilford Press. O'Cleirigh and Bullis have no disclosures to report. In the last 2 years, Otto has served as a consultant for MicroTransponder, Inc., and received research support from Shering Plough (Merck) as well as royalties from various book publishers. Pollack has served as a consultant for Eli Lilly, Medavante, Otsuka, and Targia Pharmaceuticals. In addition, he has received several grants from Bristol Myers Squibb, Euthymics, Forest Laboratories, and GlaxoSmithKline. Last, he has received equities from Medavante, Mensante Corporation, Mindsite, Targia Pharmaceutical Royalty/patent: SIGH-A, SAFER interviews. Stein has served as a consultant for Gilead, Bristol Myers Squibb, and visualmd.com.
We thank the many members of the study team who made this study a success. This includes (but is not limited to) Giselle Perez, Susan Michelson, Laura Reilly, Jessica Graham, Pamela Handelsman, Nicholas Perry, Nafisseh Souroudi, Jeffrey Gonzalez, Joseph Greer, Robert Knauz, Jonathan Lerner, Deb Herman, Luis Serpa, Jared Israel, Lauren McCarl, Allison Applebaum, Ellen Hendriksen, Christina Psaros, Lara Traeger, Sarah Markowitz, Carla Berg, the staff at BayCove, Community Substance Abuse Center, Habit OpCo, Roger Weiss, Kenneth Mayer, Robert Malow, and most importantly the study participants.
HIV continues to be a major public health concern in the United States, with no decline in rates of new infections and prevalence growing steadily (Centers for Disease Control and Prevention, 2008). The two most prevalent and interfering psychosocial comorbidities of HIV infection are clinical depression and substance use (Berger-Greenstein et al., 2007; Bing et al., 2001; Ruiz Perez et al., 2005). In a nationally representative probability sample of 2,864 adults who participated in the HIV Care Services and Utilization Study, 12-month prevalence rates for major depression, substance use without dependence (excluding marijuana use), and substance dependence were estimated as 36%, 25%, and 12.5%, respectively (Bing et al., 2001). Clinical depression and problematic substance use not only can cause significant distress and functional impairment but also can interfere with HIV treatment and care; both conditions have consistently been associated with poor antiretroviral therapy (ART) adherence (Catz, Kelly, Bogart, Benotsch, & McAuliffe, 2000; DiMatteo, Lepper, & Croghan, 2000; Lucas, Cheever, Chaisson, & Moore, 2001; Lucas, Gebo, Chaisson, & Moore, 2002; Paterson et al., 2000; Safren et al., 2001). A recent meta-analysis of 99 independent samples revealed a significant relationship between depression and adherence to HIV medications (Gonzalez, Batchelder, Psaros, & Safren, 2011).
Individuals with injection drug use (IDU) histories continue to compose a large proportion of individuals living with HIV in the United States (Centers for Disease Control and Prevention, 2010). The most recent estimates available from 37 states with confidential name-based HIV-infection reporting suggest that approximately 12% of men and 15% of women living with HIV in 2008 acquired HIV through IDU (Centers for Disease Control and Prevention, 2010). However, these estimates only account for HIV-infection directly attributable to IDU and thus do not reflect the secondary impact of transmission through sexual contact with a partner who acquired HIV through IDU.
In addition to the importance of adherence for self-care and optimization of the benefits of ART, adherence may be important in the transmissibility of HIV. HIV transmission is highly dependent on the amount of HIV viral load present in any given individual's blood and genital secretions (Hull & Montaner, 2011). HIV viral load can be reduced to an undetectable level through successful antiretroviral therapy, which seems to significantly reduce transmission risk between HIV-serodiscordant partners (Attia, Egger, Muller, Zwahlen, & Low, 2009). Accordingly, increased adherence to ART among opioid-dependent individuals living with HIV may also provide a secondary public health benefit of contributing to HIV prevention efforts (Hull & Montaner, 2011) and is a part of new emerging “test, treat, and retain” strategies.
Injection drug users living with HIV face multiple changes to successful HIV treatment, including a poorer virological response to ART compared with other HIV-infected populations, poor adherence to ART, and increased rates of attrition during interventions (Keiser et al., 2012; Weber et al., 2009). Research suggests that HIV-infected individuals currently receiving treatment for IDU or opioid dependence continue to struggle with adherence to ART (Weber et al., 2009). Avants, Margolin, Warburton, Hawkins, and Shi (2001), for example, found that more than a third of HIV-positive patients receiving methadone maintenance treatment reported less than 80% adherence to their HIV medication regimens, a rate that potentially increases the risk of developing a drug-resistant strain of the virus. Even when ART doses were directly administered and supervised in a methadone clinic-based program, continued substance use was associated with an increased risk of nonadherence and intervention dropout (Lucas et al., 2007).
HIV-positive individuals are also at an increased risk for major depression, with prevalence rates suggesting that twice as many HIV-positive individuals suffer from depression than demographically matched HIV-negative individuals (Ciesla & Roberts, 2001). Among a sample of triply diagnosed patients with HIV, substance abuse, and psychiatric illness, Berger-Greenstein et al. (2007) reported that over 70% of participants met criteria for major depression; self-reported depressive symptoms were also significantly related to worse HIV medication adherence and lower CD4 cell count. A prospective observational study by Riera et al. (2002) reported that among 202 HIV-positive patients, depression and methadone maintenance treatment were independent predictors of poor adherence to antiretroviral medications. During an evaluation of depressive symptoms and symptomatic response among HIV-infected injection drug users who were enrolled in a randomized controlled trial of directly observed ART, improvements in depression over 6 months were associated with increases in CD4 cell count and adherence, whereas worsening in depression was associated with active drug use and increases in plasma viral RNA levels (Attia et al., 2009; Hull & Montaner, 2011; Springer, Chen, & Altice, 2009).
Maintaining excellent adherence can be a difficult and challenging process for a sizeable proportion of individuals living with HIV. Poor adherence decreases the benefits of ART, as well as chances of prolonged survival (e.g., García de Olalla et al., 2002; Thompson et al., 2010). To address these barriers, there is an emerging evidence base for the efficacy of interventions for ART adherence (Amico, Harman, & Johnson, 2006; Simoni, Amico, Pearson, & Malow, 2008; Simoni, Frick, Pantalone, & Turner, 2003; Simoni, Pearson, Pantalone, Marks, & Crepaz, 2006). However, to date, most interventions have produced only modest effects, have focused directly on adherence, and have not addressed psychosocial comorbidities that may moderate the degree to which the interventions would be successful. When one considers the symptoms of a depressive episode (e.g., persistent sad mood, loss of interest, concentration problems, low energy, feelings of excessive worthlessness/guilt), it is not difficult to see how these symptoms could interfere with the acquisition or use of skills necessary to improve adherence and could potentially minimize the efficacy of adherence training interventions that do not directly treat depression.
Our prior work involved developing (Safren et al., 2004) and initially testing (Safren et al., 2009) cognitive behavioral therapy for adherence and depression (CBT-AD) in HIV. We showed in a crossover design that integrating adherence counseling using our Life-Steps protocol (Safren et al., 2001) with CBT for depression was successful at both increasing adherence and reducing depression in individuals with HIV and depression (Safren et al., 2009). Although individuals with active substance use were excluded from this initial study, HIV infection due to IDU accounts for 18.5% of cases of HIV among adults in the United States (Centers for Disease Control and Prevention, 2008, 2010). As articulated above, these individuals may be particularly at risk for depression due to the multiple stressors involved with managing comorbid HIV-infection and opioid dependence. Triply diagnosed individuals—those with HIV, clinical depression, and substance use (e.g., opioid abuse/dependence)—represent a population uniquely at risk for nonadherence. Accordingly, in designing this trial, a priori, we sought to examine the degree to which intervening on depression would assist the ability to benefit from evidenced-based adherence counseling.
The primary objective of the current study was to test, in a randomized controlled trial, CBT-AD in patients with HIV, depression, and opioid dependence who were undergoing treatment for their substance use disorder. We hypothesized that those who were assigned to the CBT-AD condition would have better adherence (primary outcome), decreased depression, and improved biological outcomes (e.g., decreased viral load and increased CD4+ lymphocyte counts) than the comparison group (enhanced treatment as usual [ETAU]) and that these gains would be maintained over the 9-month follow-up period.
Method Study Subjects and Setting
Enrollment occurred between July of 2005 and October of 2008 and included (89 randomized) individuals between the ages of 18 and 65 years who were HIV-seropositive, prescribed antiretroviral therapy for HIV, endorsed a history of injection drug use, were currently enrolled in opioid treatment for at least 1 month, and met criteria for a diagnosis of current or subsyndromal depressive mood disorder (72 major depressive disorder; one dysthymia; 16 bipolar disorder, most recent episode depressed). Subsyndromal depression (n = 10) was defined as a past history of major depression, with a current level of residual symptoms (Clinical Global Impression [CGI—see Measures section] of at least 2) that did not meet diagnostic threshold (i.e., due to antidepressant therapy).
Treatment for opioid dependence varied, with the majority of participants having received methadone (70%, n = 63) and the remainder receiving suboxone therapy (5.6%, n = 5), group (4.5%, n = 4) or individual substance abuse counseling (7.9%, n = 7), active participation in Narcotics Anonymous (4.5%, n = 4), or other active substance abuse treatment (6.7%, n = 6). There were no significant differences in type of substance abuse treatment between the experimental and control conditions.
Excluded individuals were those with any active untreated or unstable major mental illness that would interfere with study participation (e.g., active mania or psychosis), inability or unwillingness to provide informed consent, or current participation in cognitive behavioral therapy (CBT) for depression.
Participant demographics are depicted in Table 1, and raw study-related outcomes, including baseline, appear in Table 2 (note that analyses and graphs used general linear modeling [GLM] and hierarchical linear modeling [HLM] adjusted scores). The study sample was of a predominately lower socioeconomic status, with only 4% working or being in school full-time and 67% on disability. There were no differences on baseline demographic variables across conditions. There was a baseline difference for CD4 count, with the CBT-AD group having a higher CD4 count than the comparison condition, t(87) = 2.76), p < .01—see Table 2. Hence, baseline levels were covaried in longitudinal analyses.
Sociodemographic Characteristics of Participants
Unadjusted Mean Descriptive Scores for Outcomes Across Conditions and Time
The sample had substantial psychosocial comorbidity, with 62% having at least one additional Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM–IV; American Psychiatric Association, 1994) diagnosis besides depression and substance abuse disorder (see Table 2). Sixty-five percent of the randomized sample had recent illicit substance use as assessed by a combination of toxicology screening and self-report at baseline. During the clinician-administered assessments administered at baseline, participants were asked to report any substance use over the past 30 days. Approximately one third (30.3%) of randomized participants reported polysubstance use during the past 30 days, 23.6% reported alcohol use; 5.6% reported alcohol use to intoxication; 25.8% reported heroin use; 75.3% reported methadone use; 23.6% reported either opiate or analgesic use; 37.5% reported either sedative, hypnotic, or tranquilizer use; 25.8% reported cocaine use; 16% reported cannabis use; and 1.1% reported hallucinogen and inhalant use over the past 30 days; there was no reported amphetamine or barbiturate use. In addition to the clinician-administered assessment of substance use over the past 30 days, participants also provided a saliva sample for a toxicology screen. Of the 89 participants randomized, 77.2% tested positive for methadone use, 22.5% for cocaine use, 12.7% for opiate use, 8.8% for benzodiazepine use, 5% for cannabis use, and 1.3% for amphetamine and barbiturate use. There were no significant differences in reported substance use or toxicology results based on randomization condition.
For the first participants (n = 40), all study visits took place at one of four methadone clinics in the greater Boston area. Recruitment was later expanded for two major reasons: (a) we discovered that some potential participants were not comfortable referring themselves or being seen for the study at the methadone clinics despite measures to protect confidentiality about being in an HIV study, and (b) during the time of the study, more options became available for treatment of opioid dependence, such as suboxone. Seventy percent were on methadone at baseline, 6% suboxone, 7% Narcotics Anonymous/Alcoholics Anonymous only, and 18% counseling (individual or group); 56% were on an antidepressant medication at study entry Hence, participants were then recruited through community outreach and HIV clinics at Massachusetts General Hospital (MGH; n = 8) and Rhode Island Hospital (n = 9). The remaining participants (n = 32) were referred by other study participants or through community outreach and recruitment flyers posted in other HIV care or substance abuse (including additional methadone clinic) settings but were seen at an MGH-based research clinic. This adaptive trial design allowed us to keep up with the ever-changing epidemic as the trial was in process.
After a complete description of the study was provided to the participants, study clinicians obtained written informed consent. All study procedures were approved by the Institutional Review Boards at Massachusetts General Hospital (MGH) in Boston, MA and at Rhode Island Hospital in Providence, RI.
Study Design and Procedures
Study visits
After an initial evaluation to determine study eligibility and a 2-week period during which participants started using the electronic pill caps, there were four major study assessment visits: T1 was the baseline assessment; T2 was the post-treatment outcome, which happened at end of intervention for those in the experimental arm (approximately 3 months after baseline for participants in both arms); T3 was a 3-month follow-up (occurring 6 months from baseline); and T4 was a 9-month follow-up (occurring 12 months from baseline). These assessment points were chosen so that we could have four major outcome assessments over the course of 1 year of study involvement, allowing for longitudinal analyses for the follow-up assessments. The post-treatment assessment was at approximately 3 months, and that was selected to allow participants in the CBT-AD condition enough time to come to nine treatment sessions, accounting for issues such as snow and life events that might not allow for coming consistently every week. The 3-month (post-treatment) assessment was to examine acute outcomes during the treatment, right after it was discontinued. The T3 and T4 outcomes were to examine short-term and long-term maintenance of gains, as well as have time to see adherence or depression-based changes in any biological outcomes.
These assessments included electronic pill cap evaluations (Medication Event Monitoring System [MEMS], manufactured by AARDEX) for adherence, assessment of depression by self-report and an independent assessor blinded to study condition, as well as HIV plasma RNA and CD4+ lymphocyte counts either drawn for the study or abstracted from participants' medical records if collected in the month prior to the assessment. Samples acquired during the baseline assessment with a viral load of over 1,000 copies per milliliter were tested for genotypic resistance. Participants received $50.00 for the major assessments (e.g., baseline and three follow-up visits) and $25.00 for the weekly visits during the acute study period.
Randomization
Study coordinators randomly assigned participants at their first visit after the baseline in blocks of two, stratified by biological sex, depression severity (current major depression or residual symptoms only), and adherence (baseline MEMS-based adherence above or below 80%). Assignment to study condition (CBT-AD or ETAU) was concealed from both study therapists and participants until the conclusion of the first counseling visit (see below).
Assessment Measures
Primary outcome: Adherence
MEMS caps recorded each instance of bottle opening, monitoring the antiretroviral medication that the participants considered the most difficult to remember or the dose taken most frequently. To account for doses that participants may have taken without opening the pill cap (e.g., took out afternoon doses when they opened the pill bottle in the morning), we counted a dose as taken if participants could recall specific instances when they took their medications but did not use the cap (Liu et al., 2001, 2006; Llabre et al., 2006). A dose was considered missed if it was not taken within a 2-hr window of the designated time. If participants were using a pill-box prior to entering the study, we encouraged them to monitor a pill that is taken concurrently with another, with one going in the pill box and the other going in the bottle, so that the function of the pill-box (i.e., knowing if a pill has been taken/organization) could be maintained. In both conditions, if there were discrepancies between self-report and MEMs data, research assistants (RAs) or therapists would interview participants further to determine whether their cap should be replaced and/or try to figure out what may have caused this discrepancy.
For the acute outcome (baseline to post-treatment), adherence was operationalized as the percentage of MEMS-based adherence since the last visit; visits were scheduled weekly or, at most, every 2 weeks. For the follow-up longitudinal analyses, we used adherence in the past 2 weeks. This is consistent with our prior studies that used MEMs, and balances having an adequate sampling of time with not overlapping too much with the time that the participants were still potentially improving from the intervention (Safren, Hendriksen, DeSousa, Boswell, & Mayer, 2003; Safren et al., 2004; Safren et al., 2009).
Clinician-Administered Assessments
Enrollment visit
The initial evaluation to establish study eligibility included a diagnostic evaluation of DSM–IV diagnoses using the Mini International Neuropsychiatric Interview (MINI; Sheehan et al., 1998), one of the most widely used diagnostic assessments. This evaluation was completed by one of the study therapists and was presented for review and diagnostic consensus by the study team.
Independent assessments
An independent assessor (IA), who remained blind to study condition, conducted the clinician-administered outcome assessments. The IA visits included administration of (a) the Montgomery-Asberg Depression Rating Scale (MADRS; Montgomery & Asberg, 1979), and (b) a rating of global distress and impairment for depression and substance abuse using the Clinical Global Impression (CGI; National Institute of Mental Health, 1985) for severity (e.g., 1 = not ill to 7 = extremely ill). The MADRS and CGI scores were regularly reviewed through audiotape supervision with another blinded assessor. Matching the number of study visits between the two conditions helped preserve the blinding (e.g., if the IA saw the participant in the waiting room frequently, the participant could be in either condition). Additionally, participants were reminded before and during the IA visits not tell the assessor which study condition they were in.
Participant Measure of Depression
Participants completed the self-reported Beck Depression Inventory—Short Form (BDI-SF; Beck & Beck, 1972) during each visit. This measure was designed for use with medical populations, removing many of the somatic symptoms of depression that might be confounded with medication side-effects or physical functioning.
Biological Outcome Measures
At the major study assessment visits, participants who did not have an HIV plasma RNA or CD4+ lymphocyte test in the prior month accessible through clinic chart review provided blood for testing.
Intervention Conditions
Participants in both the treatment (CBT-AD) and comparison (ETAU) conditions received a single-session intervention on HIV medication adherence (Life-Steps), which involved 11 informational, problem-solving, and cognitive behavioral steps (Safren, Otto, & Worth, 1999). In each step, participants and the clinician define the problem, generate alternative solutions, make decisions about the solutions, and develop a plan for implementing them. Participants also received adherence tools such as assistance with a schedule and a cue-dosing watch that could sound two alarms per day. To enhance treatment as usual, they also had a letter mailed to their medical providers documenting the participant's depression or other psychiatric disorders and suggesting that these conditions should continue to be assessed or treated.
In addition to this, those assigned to the experimental condition also received eight sessions of CBT-AD (Safren, Gonzalez, & Soroudi, 2007a, 2007b). Accordingly, this was nine sessions total, with Life-Steps being Session 1, followed by eight CBT-AD sessions. This approach integrated continued adherence counseling with traditional CBT techniques for the treatment of depression. Module 1 (≈1 session; average in this study = 1.0 session) and provided psychoeducation about HIV and depression and a motivational interviewing (MI) exercise designed to set the stage for behavioral change. The MI exercise involved examining the pros and cons of changing and not changing self-care behaviors, as well as a discussion of a metaphor about treatment. Module 2 (≈1 session; average in this study = 1.2 sessions across participants) focused on behavioral activation and activity scheduling, which was designed to increase regularly occurring activities that involve pleasure and mastery. Module 3 (≈3 sessions; average in this study = 2.4 sessions across participants), cognitive restructuring, involved training in adaptive thinking, such as identifying and restructuring negative automatic thoughts. Module 4 (≈2 sessions; average in this study = 1.0 sessions across participants), problem-solving, involved training in selecting an action plan for problems and breaking this plan into manageable steps (Nezu, Nezu, Felgoise, McClure, & Houts, 2003). Module 5 (≈1 session; average in this study = 1.0 session across participants), relaxation, involved training in progressive muscle relaxation and diaphragmatic breathing. Some participants had a review session as needed (average = 0.4 sessions across participants). Sessions were approximately 50 min long and occurred weekly, with the goal of completion in approximately 3 months. A more detailed description of the intervention can be found in our published manuals (Safren et al., 2007a, 2007b). Flexibility in the number of sessions devoted to any module was permitted to address the complexity and variability of issues facing participants with HIV, depression, and intravenous drug use histories.
Study interventionists included clinical psychologists, psychology pre- and post-doctoral fellows in clinical psychology, and one master's-level psychologist. Training involved didactic learning from the modules and supervision using audio-recordings of sessions. To maximize therapist adherence to the intervention, all sessions were audio-recorded for monitoring and supervision. Interventionists met with a clinical supervisor on a weekly basis for clinical supervision where cases and interventions were discussed. For new interventionists, all sessions were listened to by the clinical supervisor, for at least the clinician's first participant, for feedback purposes. On an ongoing basis, one therapist per week was assigned an audiotape (of a different therapist's session) to review and complete a checklist for interventionist adherence, including whether the specific components of the modules of treatment were, in fact, delivered. Traditional monitoring of treatment fidelity, as typically done in randomized controlled trials of psychological treatments with circumscribed samples (usually individuals meeting criteria for one psychological disorder and receiving treatment for that disorder), was not possible for this study population of triply diagnosed individuals, because urgent life events often occurred and were addressed in treatment. Hence, the supervision sessions and fidelity ratings were used to develop a process for future research. This balanced the need for therapists to adhere to the general principals of CBT and intervene with respect to self-care behaviors, while being flexible regarding the order of the manualized modules, fitting the manualized modules to the clients' needs, and providing CBT strategies to assist with depression and self-care, regardless of what particular chapter would have been next in the treatment protocol. Accordingly, by the end of this process, approximately 5% of sessions were rated using the system that was developed during the study. A future article will more fully detail these data, and a current trial that seeks to examine active components of treatment (e.g., that compares this intervention to a credible, time-matched, active control treatment with both fidelity and contamination ratings) is currently underway.
After the Life-Steps adherence counseling session, participants in the ETAU condition also had eight study visits before the post-treatment assessment (to make the nine sessions total). During these visits, participants had their MEMS cap downloaded, completed the BDI-SF, and received the same number and timing of visits as those in the CBT-AD condition.
Statistical Analyses
Our prior study had an effect size of 1.0 for the primary MEMS-based adherence intent to treat outcome (Safren et al., 2009). That study, however, did not include those with comorbid substance dependence and hence we powered the study for an effect size of d = 0.8, which yielded a goal of approximately 100 participants using analysis of variance for MEMS-based adherence. Longitudinal modeling, however, as described next, allows for greater power using all available data. The actual sample size included 89 randomized participants.
HLMs (with HLM 6.06 software) were used to evaluate acute study outcomes when there were at least three data points (Raudenbush, Bryk, Cheong, & Congdon, 2004). This included MEMS-based adherence during the pre-post treatment phase, which was collected at each study visit; self-reported depression during the pre-post treatment phase, which was also collected at each study visit; and follow-up analyses for all study outcomes.
Repeated measures (GLMs) were used for pre-post assessments of depression by independent assessors (e.g., MADRS and CGI) and pre-post biological markers of HIV disease (e.g., plasma RNA viral copies and CD4 cell count), as there were only two assessment time points, and HLM or other mixed effects modeling could not be used. In these analyses, study condition assignment was the between-subjects factor. This was part of the a priori analysis plan: to first examine pre-post outcomes and second examine maintenance of any gains and biological endpoints over the longitudinal follow-up.
For pre-post HLM analyses where we had repeated measures (MEMS-based adherence and BDI), the Level 1 HLM model included the time variable (weeks since baseline), which provided the structure of the model for the outcome variable of interest. The Level 2 model tested the significance of the treatment effect and is estimated from the significance of the slope (gamma coefficient) associated with the random assignment variable (CBT-AD or ETAU). As there were significant differences between the randomly assigned conditions on CD4 cell number at study entry baseline, CD4 cell number was controlled in each of the main outcomes analyses (see footnote 1).
For maintenance of treatment effects HLM analyses, to model the slope in study outcomes across the follow-up assessments using baseline values, the Level 1 model included time, and the Level 2 model tested study condition, controlling for pre-randomization levels. Treatment effects on log viral load and CD4 cell count change across the follow-up time period were estimated by controlling for resistance to at least one antiretroviral medication in the Level 2 model.
For the HLM models, all continuous measures in the Level 2 model were centered about their group means, and all dichotomous variables were coded 1/0. Model parameters were estimated using full maximum-likelihood estimation with robust standard errors. In all analyses that used HLM, unconstrained models were run to confirm significant individual variation about the slope and intercept before accounting for random assignment. For all analyses, the Type I error rate adopted was a p of .05. Effect sizes for HLM growth estimates were calculated for statistically significant outcomes using the formula dGMA-raw = γ11(time)/SDraw, in line with current recommendations for communicating effect magnitude (Feingold, 2009; Raudenbush & Liu, 2001).
For all analyses involving comparing the two study arms, analyses presented controlled for baseline CD4 cell count differences between groups (there were no other baseline differences between the two study conditions). When the same analyses were conducted not controlling for baseline CD4 cell count differences between groups, the pattern of results were the same (see footnote 1).
Results Participant Characteristics
Participant flow throughout the duration of the study is depicted in Figure 1. Ninety-one percent of those randomized were retained for the acute outcome assessment (n = 86), and 84% (n = 79) returned to at least one post-treatment follow-up and hence could be used for follow-up analyses. Although there was a raw number greater loss to attrition at the final assessment point in the ETAU condition (eight out of 44 participants), compared with the CBT-AD condition (15 out of 45 participants), this difference was not statistically significant. Raw scores for study outcomes are presented in Table 2 by study condition (note that HLM and GLM analyses use adjusted scores). At baseline, 11.2% of the sample collapsed across condition had genotypic resistance. There were no study-related adverse events that occurred during the study.
Figure 1. Consolidated Standards of Reporting Trials (CONSORT) participant flow chart. IDU = injection drug use; CBT-AD = cognitive behavioral therapy for adherence and depression; ETAU = enhanced treatment as usual.
Baseline to Post-Treatment Outcomes
Adherence (MEMS)
There was a significant upward slope in MEMS-based adherence, γslope = 0.47, t(88) = 2.16, p = .033, during the treatment period, indicating improved adherence for the study participants as a whole. In addition, there was significant individual variation about the slope, ρslope = 2.35, df(87), χ2 = 203.01, p < .001, providing the justification for conducting the analysis by randomized group (Level 2 analysis). When adding treatment condition, and, as a covariate, baseline CD4, to the model, the increase in adherence was significantly greater over time in the CBT-AD condition (11.8 percentage points) than in the comparison condition (0.5 percentage points), γslope = 0.887, t(86) = 2.38, p = .02, dGMA-raw = 0.64 (see Figure 2).
Figure 2. Pre-post outcomes: Longitudinal (hierarchical linear modeling) analysis of MEMS-based adherence and depression (BDI-SF). CBT-AD = cognitive behavioral therapy for adherence and depression; ETAU = enhanced treatment as usual; MEMS = Medication Event Monitoring System; BDI-SF = Beck Depression Inventory—Short Form (Beck & Beck, 1972). Data points are adjusted scores using MEMS-based adherence and BDI-SF scores for the time since prior visit.
Depression rated by independent assessor
There were significantly greater reductions in depression for the CBT-AD condition relative to the comparison condition for both the MADRS, F(1, 78) = 9.72, p = < .01, d = 0.55, and CGI, F(1, 78) = 17.14, p < .001, d = 0.85 (see Table 2). These analyses used baseline CD4 as a covariate.
Self-reported depression (BDI-SF)
There was a significant decreasing slope, γslope = –0.23, t(88) = –3.52, p < .01, for self-reported depression over time. When accounting for treatment condition, and controlling for baseline CD4 as a covariate, those in the CBT-AD condition experienced a significant estimated reduction in depression symptoms (5.1 points on the BDI-SF) compared with a nonsignificant change in the control condition (<1 point on the BDI-SF), γslope = –0.320, t(86) = –2.39, p = .02, dGMA-raw = 0.63.
Biological outcomes
Although treatment effects on HIV viral load and CD4 count were more likely to emerge only at follow-up, acute effects (e.g., baseline to post-treatment) were examined and are presented in Table 2 for consistency. Controlling for medication resistance, randomized effects were not significant for either CD4 cells, F(1, 74) = 1.92, p = .32, d = 0.10, or for log viral load, F(1, 71) = 1.08, p = .30, d = 0.20, with the additional control for baseline CD4.
Post-Intervention Follow-Up Assessments at 3 and 9 Months
Adherence (MEMS)
MEMS-based adherence gains acquired across CBT-AD treatment were not maintained during follow-up as evidenced by the significant downward slope for MEMS-based adherence, γslope = –0.294, t(79) = –3.24, p < .01, that did not differ by study condition, γslope = 0.20, t(76) = 1.23, p = .22, dGMA-raw = 0.21 (see Figure 3). Baseline CD4 differences were controlled for as a covariate in these analyses.
Figure 3. Follow-up outcomes: Analysis of MEMS-based adherence and depression (BDI-SF). CBT-AD = cognitive behavioral therapy for adherence and depression; ETAU = enhanced treatment as usual; MEMS = Medication Event Monitoring System; BDI-SF = Beck Depression Inventory—Short Form (Beck & Beck, 1972); F/U = follow-up. Follow-up outcomes used hierarchical linear modeling adjusted scores for prior 2 weeks MEMS-based adherence.
Clinician-assessed depression (MADRS and CGI)
Depression gains as assessed by blinded clinicians were maintained, which was demonstrated on the CGI by a trend for continued improvement during the follow-up time period, γslope = –0.008, t(80) = –1.93, p = .06, with no differential improvement by condition over follow-up after acute treatment ended, γslope = 0.011, t(78) = 1.59, p = .12, dGMA-raw = 0.51. Similarly, the MADRS also demonstrated a trend for a continued improvement for the group as a whole, γslope = –0.052, t(80) = –1.69, p = .09, with no differential improvement by condition, γslope = 0.078, t(78) = 1.47, p = .14, dGMA-raw = 0.61. These analyses had baseline depression scores and baseline CD4 as covariates.
Self-reported depression (BDI-SF)
Improvements in depression acquired during the CBT-AD intervention and assessed via self-report were maintained during follow-up, as evidenced by no significant change in self-reported depressive symptoms for the whole sample, γslope = 0.03, t(73) = –1.27, p = .21, or by group assignment when controlling for baseline BDI and CD4 differences, γslope = –0.01, t(72) = –0.30, p = .76, dGMA-raw = 0.13.
Biological outcomes (HIV viral load, CD4 cell count)
There was no significant change in log viral load over the follow-up time period for the group as a whole, γslope = –0.0015, t(75) = –0.40, p = .69, or based on group assignment, γslope = 7.28 × 10−x4, t(74) = –0.165, p = .87, dGMA-raw = 0.02, controlling for baseline log viral load, CD4 cell number, and resistance. There were also no differences across the two conditions in the percentage of participants who attained a suppressed viral load, γslope = 5.0 × 10−x5, t(74) = –0.002, p = .98, dGMA-raw < 0.01.
Over the follow-up period, the slope of CD4 cell number was nonsignificant for the entire sample, γslope = 0.590, t(79) = 1.08, p = .29. When condition was added to the model, there was a significant increase in CD4 cells in the CBT-AD condition compared with the control condition, controlling for baseline CD4 and medication resistance, 61.2 CD4 cell increase versus 22.4 CD4 cell decrease, γslope = 2.09, t(76) = 2.20, p = .03, dGMA-raw = 0.60. Note these differences were also significant without controlling for baseline CD4 and medication resistance.
DiscussionThe current study examined the use of a time-limited intervention (CBT-AD) addressing both adherence and clinical depression in a sample of triply diagnosed individuals with HIV. The intervention had acute and significant effects on both adherence and depression during the time in which the intervention was being delivered. MEMS-based adherence in the CBT-AD group improved approximately 11.8% from baseline and 11.3% over the comparison condition during treatment. The magnitude of this effect is potentially clinically significant in that it has been suggested that a 10% change in adherence can result in improved HIV outcomes (Bangsberg, 2006; Liu et al., 2001). However, after the intervention ended, MEMS-based adherence decreased, whereas intervention-related improvements in depression remained relatively stable. By way of contrast, our prior study of CBT-AD with depressed HIV-infected participants who were not also struggling with IDU histories and substance dependence showed sustained effects on both adherence and depression, and improvements in viral load over time (Safren et al., 2009). As such, it appears that our intervention was not resilient to the psychosocial challenges magnified by the context of opioid dependence, HIV, and depression. It is not clear which aspect or aspects of substance dependence (e.g., lapses in substance use, neuropsychological impairment, psychosocial stress, schedule disruptions, altered motivation, altered meaning of pill taking) may have contributed to this loss of efficacy for the adherence but not depression outcomes, and moderators will be explored in future secondary articles. Our findings indicate that, for triply diagnosed individuals, continued adherence counseling may be necessary to maintain or potentially augment adherence gains, even when depression symptoms improve.
With respect to the depression findings, the average depression score on the MADRS for those in the CBT-AD conditions at baseline was in the range for “moderate” severity and only “mild” at the 12-month follow-up assessment. This 40% decrease in symptoms at post-treatment, which was maintained at 12 months, represents a clinically meaningful reduction (Müller, Himmerich, Kienzle, & Szegedi, 2003; Robertson, 1983).
Differences in viral load did not emerge across the two conditions over time; however, there were differential improvements in CD4 in those who received CBT-AD versus those who received ETAU when statistically controlling for baseline group differences. Although preliminary, this finding is consistent with the results of other HIV-related psychosocial interventions demonstrating that psychosocial interventions can directly improve biological indicators of HIV pathogenesis, including CD4 cells counts (Petrie, Fontanilla, Thomas, Booth, & Pennebaker, 2004) and HIV viral load (Antoni et al., 2006). This finding, that there were differences in depression and CD4 but not long-term differences in adherence or viral load is noteworthy but certainly requires replication. Despite the absence of viral load change, it is possible that our participants still reaped some psychoneuroimmunological benefit due to sustained reductions in depression as evidenced by an increase in CD4 lymphocyte count over time, which occurred after covarying out baseline differences. The reduction of the immunosuppressive effects of depression-related dysregulation of the catecholaminergic (norepinephrine) or HPA (cortisol) axis may support sustained increases in CD4 cell counts (Leserman, 2003). In fact, Antoni et al. (2006) reported that treatment-related decreases in HIV viral load were mediated by reductions in depressed mood. The preliminary finding that treating depression is associated with improved immunity is also consistent with noninterventional studies showing a relationship between depressive symptoms and change in CD4 cells over time (Ickovics et al., 2001; Ironson et al., 2005; Leserman, 2008). Future secondary analyses will examine mediational or other potential pathways with these data. This immunological finding requires replication, as it is possible that there are other explanations for these results, such as regression to the mean after different values at baseline. Additional randomized controlled trials of interventions that improve depression on immunologic outcomes are required to examine this further. With respect to the absence of viral load findings, it is possible that the ability to detect viral load differences was limited because of the relatively low average viral load levels at baseline.
It is noteworthy that prior studies have found short-term ART adherence gains simply as a result of using a MEMS cap for monitoring without additional intervention, gains sometimes lasting up to 40 days (Deschamps, Van Wijngaerden, Denhaerynck, De Geest, & Vandamme, 2006; Wagner & Ghosh-Dastidar, 2002); although our post-treatment outcome assessment target time window was 90 days. Accordingly, it is possible that the first set of MEMS-based adherence assessments (at baseline—first 2 weeks) represent an improvement over what their true baseline adherence would have been. Hence, decreases in MEMS-based adherence over time to the final follow-up may be due to the waning effects of adherence monitoring with MEMS on adherence over time. The randomized design with the post-treatment outcome being approximately 3 months after randomization (i.e., more than 40 days) showed differences between the intervention and comparison conditions at post-treatment, indicating that it is unlikely that there would be a difference in a sensitization effect to MEMS-based adherence across the two study conditions.
There were eight out of 44 individuals lost to follow-up in the CBT-AD condition and 15 out of 45 from the ETAU condition. Although this was not statistically significant, it raises an issue that may be common to adherence interventions. More specifically, if depression is associated with adherence, and those who are adherent to study participation are more likely to also be adherent to HIV medications, it is possible that attrition is associated with poorer adherence and more severe depression. In this scenario, the actual treatment effect would be greater than what was observed, because the control participants who dropped out would be those with worse depression and adherence. Conversely, it is also possible that control participants who dropped out did so because they did not perceive benefit from participation in the study. Our clinical experience meeting with these participants, however, suggests that that the latter was not the case.
There are several additional limitations to the present study that should be noted. First, although MEMS are an objective indicator of adherence, it is possible that adherence was underestimated if the MEMS cap was not used. Accordingly, we asked participants at each assessment whether they recalled taking pills without using the cap and used a corrected adherence score (Liu et al., 2001, 2006; Llabre et al., 2006). Second, as part of a program of research to test a treatment, and to examine whether treating depression is necessary to benefit from an adherence intervention before trying to dismantle the “active ingredients,” the comparison group was not attention-matched. Although CBT-AD and ETAU participants came for the same number of visits and had the same incentive payments, we do not know if the positive acute adherence outcomes are related to specific elements of CBT-AD beyond the Life-Steps adherence counseling. Third, despite procedures to ensure confidentiality, we discovered that individuals in methadone treatment centers may have been reluctant to refer themselves to the study because of the fear of having their HIV status “outed.” Recruitment efforts that were expanded to address this barrier threatened internal validity, but this concern was balanced by achieving greater participant heterogeneity and therefore generalizability. Fourth, the participant incentives may have driven participation, and generalization of findings to opioid-stabilized patients who are less likely to come for such counseling may be limited. Fifth, we had hoped for a sample size of 100, and only 89 could be randomized because of the complexities of recruitment. Finally, given the complexity of the population, monitoring fidelity to the intervention was a work in progress throughout the study. Accordingly, by the later half of the study, we had developed a fidelity monitoring checklist that allowed for therapist flexibility when emergent life events would occur, with the ability to change the order of modules and/or utilize the treatment module most relevant to an emergent concern when it would occur. Although this can be seen as a threat to internal validity, it conversely increases the external validity of the intervention, as this is what clinicians would likely do in real-world clinical practice.
Future research should examine the cost-effectiveness of the intervention as part of a larger effectiveness trial involving substance abuse treatment programs that have high numbers of HIV-infected patients in their practice base. Accordingly, if substance abuse counselors or less trained interventionists could integrate this intervention into their current counseling, there would not be incremental costs, and adherence counseling could be maintained, potentially allowing for sustained benefits.
The findings, however, suggest that CBT-AD is a potentially useful strategy for increasing adherence and decreasing depression in HIV-positive patients with a history of IDU who are in substance abuse treatment. Adherence gains were only present during the time that the treatment was ongoing, suggesting that booster, additional, or continued adherence counseling sessions may be needed to sustain improvements in adherence outcomes in this population of individuals struggling with multiple comorbidities. It appears that this intervention, integrated into substance use counseling in methadone or other drug treatment programs, would be beneficial for individuals with HIV and substance abuse disorders. For depression, this intervention resulted in sustained improvement, and CD4 cell counts increased in the CBT-AD condition compared with the control condition over time. Whereas prior studies have found correlational associations of depression to CD4, this is the first study to suggest a possible effect when depression was successfully treated.
Footnotes 1 We thank the anonymous reviewers for suggesting that we control for CD4 in all outcome analyses, as CD4 differed in magnitude between study arms. Analyses were initially conducted without controlling for baseline CD4, and the pattern of results was the same when controlling for and when not controlling for CD4 in these analyses.
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Submitted: May 9, 2011 Revised: February 6, 2012 Accepted: February 7, 2012
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Source: Journal of Consulting and Clinical Psychology. Vol. 80. (3), Jun, 2012 pp. 404-415)
Accession Number: 2012-10794-001
Digital Object Identifier: 10.1037/a0028208
Record: 13- Title:
- Comparing criterion- and trait-based personality disorder diagnoses in DSM-5.
- Authors:
- Yam, Wern How. Department of Psychology, University at Buffalo, The State University of New York, Buffalo, NY, US, whyam@buffalo.edu
Simms, Leonard J.. Department of Psychology, University at Buffalo, The State University of New York, Buffalo, NY, US - Address:
- Yam, Wern How, Department of Psychology, Park Hall 219, University at Buffalo, The State University of New York, Buffalo, NY, US, 14221, whyam@buffalo.edu
- Source:
- Journal of Abnormal Psychology, Vol 123(4), Nov, 2014. pp. 802-808.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 7
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- DSM-5, classification, assessment, Section III, personality disorder
- Abstract:
- In the recent Diagnostic and Statistical Manual of Mental Disorders (DSM-5), the official personality disorder (PD) classification system remains unchanged. However, DSM-5 also includes an alternative hybrid categorical-dimensional PD system in Section III to spur additional research. One defining feature of the alternative system is the incorporation of a trait model with PD-specific trait configurations, but relatively little work has evaluated how these traits map onto official PD diagnoses or their implications for diagnosis rates. To that end, we compared official PD criteria to Section III PD traits in a sample of current or recent psychiatric patients. We (a) evaluated the extent to which PD traits predicted traditional PD criterion counts, and (b) computed trait-based diagnosis rates and compared them to those reported in several published outpatient and epidemiological samples. Overall, PD traits generally predicted PD criterion counts, but with less than ideal specificity. In addition, we identified differences in diagnosis rates across approaches. These results provide some support for the Section III approach, but they also identify important areas in need of refinement and future study before the field could reasonably switch to a hybrid PD classification approach like that in Section III. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Diagnostic and Statistical Manual; *Personality Disorders; *Personality Traits; Criterion Referenced Tests
- PsycINFO Classification:
- Personality Disorders (3217)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Structured Clinical Interview for DSM–IV Axis II Disorders Personality Questionnaire
Personality Inventory for DSM-5 DOI: 10.1037/t30042-000 - Grant Sponsorship:
- Sponsor: National Institute of Mental Health, US
Grant Number: R01MH080086
Recipients: Simms, Leonard J. - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Aug 11, 2014; Accepted: Jul 7, 2014; Revised: Jun 24, 2014; First Submitted: Sep 30, 2013
- Release Date:
- 20140811
- Correction Date:
- 20141110
- Copyright:
- American Psychological Association. 2014
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0037633
- Accession Number:
- 2014-32647-001
- Number of Citations in Source:
- 31
- Persistent link to this record (Permalink):
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- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-32647-001&site=ehost-live">Comparing criterion- and trait-based personality disorder diagnoses in DSM-5.</A>
- Database:
- PsycINFO
Comparing Criterion- and Trait-Based Personality Disorder Diagnoses in DSM-5 / BRIEF REPORT
By: Wern How Yam
Department of Psychology, University at Buffalo, The State University of New York;
Leonard J. Simms
Department of Psychology, University at Buffalo, The State University of New York
Acknowledgement: This study was supported by a research grant to L. J. Simms from the National Institute of Mental Health (No. R01MH080086).
Substantial evidence has accumulated showing the limitations of the Diagnostic and Statistical Manual of Mental Disorders (DSM) classification system for personality disorder (PD) and the advantages of understanding PD from a trait-dimensional perspective (e.g., Clark, 2007; Livesley, 2007; Trull & Durrett, 2005; Widiger & Samuel, 2005; Widiger & Trull, 2007). Despite these advances, the official PD classification remains unchanged in the revised DSM manual (DSM-5; American Psychiatric Association [APA], 2013). However, the Personality and PD workgroup proposed an alternative hybrid PD classification system that combines dimensional and categorical elements, reflecting a compromise between those favoring a fully trait-based system (e.g., Clark, 2007; Widiger & Simonsen, 2005) and those favoring the polythetic categorical system embodied in the official nosology since 1980 (e.g., Gunderson, 2010; Zimmerman, 2012). To spur research into this alternative, DSM-5 lists the hybrid approach in Section III, but relatively little is known about how Section III PDs compare to their traditional counterparts. Thus, our principal aim was to compare the trait-based elements of the alternative approach to the criterion-based elements of the traditional PD classification system in terms of diagnostic validity and diagnosis rates.
The Section III approach retains 6 of the 10 traditional PDs—antisocial, avoidant, borderline, narcissistic, obsessive–compulsive, and schizotypal PDs. PDs are conceptualized as significant impairments in personality functioning coupled with pathological personality traits that are assessed across seven diagnostic criteria (APA, 2013). Criterion A evaluates personality functioning, comprising self- and interpersonal impairments unique to each PD. Criterion B includes 25 personality traits—nested within five broad domains (negative affect, detachment, antagonism, disinhibition, and psychoticism)—that can be present in prespecified configurations associated with each retained PD or in idiosyncratic configurations (i.e., PD trait-specified [PD-TS]). For example, a diagnosis of borderline PD requires at least four of the seven specified traits—anxiousness, depressivity, emotional lability, hostility, impulsivity, risk-taking, and separation insecurity—with impulsivity, risk-taking, or hostility required. Criteria C to G include inclusion and exclusion criteria similar to the official system.
Given the novelty of the Section III approach, relatively little has been published evaluating its utility and functioning vis-à-vis the official criterion-based model. However, two recent papers reported limited support for the particular trait-to-PD mappings suggested in Section III (Few et al., 2013; Hopwood et al., 2012). In both cases, although evidence showed that the Section III traits generally predicted official PD criterion counts, the specified trait-to-PD relations showed limited specificity, with (a) nonspecified traits sometimes adding significantly to the prediction of certain PDs and (b) specified traits often relating to multiple PDs. Although the nonspecificity of traits is not surprising given the known patterns of comorbidity among the traditional PDs, these findings nonetheless raise important questions about the adequacy of the Section III trait configurations. Moreover, inconsistencies across the studies suggest that additional work is needed before strong conclusions are possible regarding how best to map PD traits onto traditional PDs.
A related question not adequately addressed in Section III is the clinical threshold required for traits, which has important diagnostic implications. Notably, no specific thresholds are provided in Section III except that trait elevation could be assessed by comparison to “population norms and/or clinical judgment” (p. 774; APA, 2013); the use of normative samples also has been suggested previously as a means of interpreting trait scores via standardized T scores (Miller, 2012). Samuel, Hopwood, Krueger, Thomas, and Ruggero (2013) recently studied the effect of the proposed clinical thresholds on PD diagnosis rates in a large sample of undergraduates. Using norms reported by Krueger, Derringer, Markon, Watson, and Skodol (2012), Samuel et al. reported markedly lower diagnosis rates for trait-based diagnoses relative to the traditional criterion-based diagnoses. Although provocative, the data of Samuel et al. were collected in undergraduates; extension to patient samples is needed to study the generalizability of their results to more ecologically valid samples.
Although the use of trait configurations as a dimensional bridge to the traditional criterion-based approach is not a new practice (e.g., Miller, 2012), an issue in need of further study is the extent of classification convergence across models. Morey and Skodol (2013) examined classification convergence through mental health clinicians who rated a prior patient using the trait- and criterion-based models. Results showed general support for classification convergence, with correlations between DSM-5 and DSM–IV criterion counts yielding a Mdn correlation of .75. However, Morey and Skodol’s approach was limited to clinician reports of patient symptomatology. A study reporting Section III diagnosis estimates and classification convergence from the patient perspective would meaningfully extend the literature.
In sum, more work is needed to improve our understanding of intermodel convergence and diagnosis rates across PD classification approaches, with the aim of informing future revisions to PD nosology. As such, the goals of the study presented here were to study (a) how strongly Section III PD traits predict traditional PD criterion counts, (b) whether any nonspecified PD trait incrementally predicts PD criterion counts, and (c) Section III diagnosis rates in a large psychiatric sample.
Method Participants and Procedures
Participants—recruited by distributing flyers at mental health clinics across western New York—were eligible to participate if they reported psychiatric treatment within the past 2 years. The final sample included 628 participants, 454 of who completed the measures needed for this study—M age = 42.0 years (SD = 12.6), 65% female, 68% Caucasian. This subsample differed from the full sample in terms of race, χ2(4, N = 624) = 28.75, p < .01, and age, t(626) = 3.68, p < .01, but not sex, χ2(1, N = 627) = 1.56, p = .21. Those excluded were more likely to be African American and older. Eligible participants attended a 4-hr session and completed self-report measures using computers in privacy carrels. For the study presented here, we analyzed responses to the Personality Inventory for DSM-5 (PID-5; Krueger et al., 2012) and Structured Clinical Interview for DSM–IV Axis II Disorders Personality Questionnaire (SCID-II-PQ; First, Spitzer, Gibbon, & Williams, 1995). Participants were compensated $50 plus transportation costs. Procedures were approved by the Social and Behavioral Sciences Institutional Review Board at the University at Buffalo.
Measures
The PID-5 assesses the 25 maladaptive traits proposed in Section III. It includes 220 self-report items rated on a 4-point scale ranging from 0 (very false or often false) to 3 (very true or often true). Higher scale scores are indicative of greater pathology. Internal consistencies in the current study averaged .87, range = .75 to .96, across traits. Recent studies have supported the construct validity of the PID-5 (Anderson et al., 2013; Hopwood et al., 2013; Wright & Simms, 2014).
The SCID-II-PQ is a self-report measure composed of 119 items rated dichotomously, which typically is administered before the SCID-II interview to shorten the interview period, that map onto DSM–IV PD criteria. Internal consistencies in the current study averaged .69, range = .50 to .81, across PD criterion counts. Past work has supported the use of the SCID-II-PQ as a standalone measure of PD features (Ekselius, Lindström, von Knorring, Bodlund, & Kullgren, 1994; Jacobsberg, Perry, & Frances, 1995; Piedmont, Sherman, Sherman, & Williams, 2003).
Data Analyses
Analyses were restricted to the six retained Section III PDs. Analyses first were conducted to evaluate the extent to which Section III traits predicted PD criterion counts. SCID-II-PQ criterion counts were correlated with PID-5 traits, followed by multiple regressions with hierarchical and stepwise components. SCID-II-PQ criterion counts were regressed on separate blocks of specified and nonspecified traits. In the first block of each analysis, specified PID-5 traits were included as predictors of a single SCID-II-PQ criterion count. The second block—using a stepwise selection process in which criteria for entry and retention in the regression model were set at p < .01—included all remaining nonspecified traits. These procedures permitted us to study the predictive power of PID-5 traits individually and in the context of other traits vis-à-vis SCID-II-PQ criterion counts. Given the many tests conducted, we adopted a moderately conservative significance threshold of p < .01 across all analyses.
Second, analyses were conducted to study how the Section III trait model affects diagnostic rates relative to the official criterion-based model. Traditional criterion-based diagnostic rates were obtained from previously published psychiatric (Alnaes & Torgersen, 1988; Zimmerman, Rothschild, & Chelminski, 2005) and epidemiological samples (Grant et al., 2003; Torgersen, 2005; Trull, Jahng, Tomko, Wood, & Sher, 2010) whereas Section III PD diagnoses in our sample were scored using the specific DSM-5 PD trait configurations. For the latter, T scores (i.e., standardized scores in which M = 50 and SD = 10) were computed using U.S. representative norms presented by Krueger et al. (2012). Consistent with the general personality assessment literature, we considered a T score of 65 or greater to reflect clinical significance. χ2 tests then were conducted to examine significant differences in diagnosis rates across studies.
Results Convergence Between Traits and PD Criterion Counts
Table 1 presents correlations between SCID-II-PQ criterion counts and PID-5 traits. Results revealed strong relations between PID-5 traits and SCID-II-PQ criterion counts. Across diagnoses, PD-specified traits generally correlated moderately to strongly with SCID-II-PQ criterion counts, Mdn r = .43, range = .13 to .66. However, it is important to note that 5 of 30 PD-specified traits showed relatively weak correlations with their respective SCID-II-PQ criterion counts, including intimacy avoidance for avoidant PD, risk-taking for borderline PD, intimacy avoidance and restricted affectivity for obsessive–compulsive PD, and restricted affectivity for schizotypal PD. In addition, many nonspecified traits correlated weakly to moderately with SCID-II-PQ criterion counts, Mdn r = .26, range = .01 to .57. Across PDs, 3, 11, 12, 15, 1, and 11 nonspecified PID-5 traits correlated at least moderately with SCID-II-PQ criterion counts for antisocial, avoidant, borderline, narcissistic, obsessive–compulsive, and schizotypal PDs, respectively. Thus, most PD-specified traits correlated as expected, but many additional traits also related significantly across PDs, suggesting that the DSM-5 trait specifications may be incomplete in some cases.
Zero-Order Correlations Between SCID-II-PQ Criterion Counts and PID-5 Traits
Hierarchical regressions, in which corresponding SCID-II-PQ criterion counts were regressed on blocks of specified and nonspecified traits, then were conducted to determine the strongest trait predictors of each PD in the context of other traits. The average tolerance and variance inflation factors across the regressions were greater than .10 (M = .55, SD = .14) and lower than 10 (M = 1.97, SD = .59), respectively, indicating that multicollinearity did not exert undue influence on parameter estimates. Regression results (see Table 2) indicated that all PD-specified trait blocks significantly predicted their corresponding SCID-II-PQ criterion counts, Mdn R2 = .33, range = .21 (antisocial) to .54 (borderline). The stepwise addition of nonspecified traits showed incremental prediction of all criterion counts except obsessive–compulsive PD, Mdn ΔR2 = .03, range = .00 (obsessive–compulsive) to .12 (narcissistic).
SCID-II-PQ Criterion Counts Regressed on PID-5 Specified and Nonspecified Traits
For Block 1, PD-specified traits varied in the extent to which they predicted their respective SCID-II-PQ criterion counts. Across PDs, 15 of the 30 predicted traits were significant predictors of their corresponding criterion counts. For antisocial PD, only callousness predicted the corresponding criterion count. Three of four specified traits for avoidant PD (anhedonia, anxiousness, and withdrawal) predicted the corresponding criterion count. For borderline PD, four of seven specified traits (depressivity, emotional lability, hostility, and impulsivity) predicted the matching criterion count. Both specified traits for narcissistic PD (attention seeking and grandiosity) predicted the corresponding criterion count. For obsessive–compulsive PD, only rigid perfectionism predicted the matching criterion count. Finally, four of six specified schizotypal PD traits (eccentricity, restricted affectivity, unusual beliefs and experiences, and withdrawal) predicted the corresponding criterion count, with restricted affectivity predicting negatively.
Block 2 stepwise selection procedures added nonspecified traits to all but one model (obsessive–compulsive PD). Furthermore, the standardized parameter estimates indicated that specified traits for each PD still varied in how they predicted SCID-II-PQ criterion counts. Of the 15 significantly predicting specified traits in Block 1, 12 remained significant after accounting for additional nonspecified traits. For antisocial PD, one specified trait (callousness) and three nonspecified traits (grandiosity, submissiveness, and unusual beliefs/experiences) predicted the corresponding criterion count, with grandiosity and submissiveness predicting negatively. For avoidant PD, two specified traits (anxiousness and withdrawal) and three nonspecified traits (grandiosity, manipulativeness, and separation insecurity) predicted the corresponding criterion count, with grandiosity and manipulativeness predicting negatively. For borderline PD, three specified traits (depressivity, emotional lability, and impulsivity) and two nonspecified traits (submissiveness and suspiciousness) predicted the corresponding criterion count, with submissiveness predicting negatively. For narcissistic PD, both specified traits (attention seeking and grandiosity) and two nonspecified traits (hostility and suspiciousness) predicted the corresponding criterion count. Finally, for schizotypal PD, three specified traits (eccentricity, unusual beliefs and experiences, and withdrawal) and one nonspecified trait (emotional lability) predicted the corresponding criterion count.
Diagnosis Rates
Table 3 presents diagnosis rates for the Section III trait approach in our patient sample and traditional PDs in published outpatient and epidemiological samples. Results showed a mixed picture. Compared with the outpatient samples, our trait-based diagnosis rates for avoidant and obsessive–compulsive PDs were lower, diagnosis rates for borderline and narcissistic PDs were comparable, and antisocial and schizotypal PDs yielded mixed results, with trait-based diagnosis rates either higher or comparable to their traditional counterparts. Compared with the epidemiological samples, trait-based diagnosis rates were higher than their traditional counterparts in all cases except antisocial and obsessive–compulsive PDs.
PD Diagnosis Rates (%) in the Current Study and Published Samples
DiscussionWe compared the trait- and criterion-based approaches to PD classification in DSM-5 to study (a) the performance of the PD-specified traits in predicting official criteria, (b) whether additional nonspecified traits predicted official criteria, and (c) diagnosis rates of the proposed trait configurations relative to other published outpatient and epidemiological samples. We showed that PD-specified and nonspecified traits generally predicted traditional PD criteria. However, the results also revealed problems with specificity, with some PD-specified traits predicting multiple traditional PDs and some nonspecified traits incrementally predicting traditional PDs, even after accounting for PD-specified traits. The findings also demonstrated discrepancies across models in diagnosis rates. Taken together, the results suggest that the Section III trait model will require further study and, perhaps, modifications before the Section III system can be considered a parallel replacement for the current PD nosology.
Associations Between Traits and Types
Consistent with recent literature (Few et al., 2013; Hopwood et al., 2012; Morey & Skodol, 2013; Samuel et al., 2013), our results suggest that traits relate broadly to official criterion counts. However, PD-specific trait findings were more mixed. Although all PDs were predicted by one or more traits, some PD-specified traits did not behave as expected. In particular, the regressions revealed some results that either (a) failed to conform to the predictions of Section III trait specifications or (b) did not appear particularly meaningful. In some cases, nonspecified traits incrementally predicted a given PD in conceptually meaningful ways. For example, hostility incrementally predicted narcissistic PD after accounting for grandiosity and attention-seeking, a finding that is consistent with models of pathological narcissism (e.g., Pincus et al., 2009). In other cases, nonspecified traits showed evidence of incremental prediction for less clear reasons. For example, submissiveness correlated positively with borderline PD but showed an opposite pattern in the regression results, a finding that suggests that suppression could be causing some spurious effects (e.g., see Beckstead, 2012). Finally, likely because of overlapping variance across traits, some PD-specified traits that correlated significantly with their corresponding PD failed to show the same pattern in the regressions. For example, all seven PD-specified traits for borderline PD were significant in the correlation analyses, but only four of seven (depressivity, emotional lability, hostility, and impulsivity) were significant predictors in the regressions. Results such as these suggest that more parsimonious subsets of traits may more efficiently represent some PDs.
Nonetheless, these results provide an empirical basis for considering possible revisions to the Section III trait specifications. Of course, replication is needed, especially for results that lack a clear conceptual basis. Furthermore, because the PID-5 represents only one instantiation of Section III traits, other assessment tools (e.g., the Computerized Adaptive Test of Personality Disorder; see Simms et al., 2011) could be used to determine whether the observed effects generalize beyond the PID-5. Taken together, these findings echo the critique that empirically supported conceptualizations of traits that personify PD in Section III are lacking (Zimmerman, 2012). In guiding future work, one suggested data-driven approach to identifying traits for a given PD would involve selecting traits that are above a minimum convergent correlation but that also exhibit discriminant validity relative to other PDs (Hopwood et al., 2012). Such an approach would identify an empirical set of traits to define each PD while also addressing the problem of diagnostic overlap that has plagued the traditional criterion-based approach (e.g., Clark, 2007; Westen & Shedler, 1999).
Diagnosis Rates Across Approaches
Our results revealed notable diagnosis rate differences across approaches. Relative to traditional diagnosis rates in published outpatient samples, the trait-based elements of Section III resulted in a mixed picture, with some trait-based diagnoses being more, less, or equivalently diagnosed. Notably, similar to Samuel et al. (2013), our trait-based diagnosis rates likely represent overestimates to the extent that other inclusion and exclusion criteria were not considered (e.g., evidence of impairment, exclusions due to substance abuse or general medical conditions, etc.). As such, although further study is needed to examine this point, it is quite likely that the Section III diagnoses that account for all impairment, inclusion, and exclusion criteria will be more conservative than comparable traditional PD diagnoses. Such shifts in prevalence rates may have a large effect on the broader mental health-care system. The relative diagnostic conservatism of the Section III trait-based approach likely will lead to a surge in subthreshold diagnoses and, consequently, an increase in PD-TS diagnoses.
Of course, the traditional approach is not without a similar dynamic. Coverage inadequacies are a weakness of the official PD nosology (Widiger & Trull, 2007), likely resulting in the excessive PD Not Otherwise Specified (NOS) diagnoses reported to be among the most prevalent in psychiatric samples (e.g., Zimmerman et al., 2005). Therefore, both approaches come with a generic, nonspecific option for diagnosing PD. However, the PD-TS diagnosis has the advantage of specifying in a standardized way the exact traits that are impairing to a given patient, which could lead to the development of trait-based treatment approaches.
Limitations and Conclusion
This work extends the PD literature in important ways but is not without limitations. First, although we studied a large sample of current or recent psychiatric patients, which is an important extension of previous work in this area, all participants were limited geographically and mainly were Caucasian and African American, which may limit the generalizability of our findings. Second, as noted above, our diagnosis rates are limited to the extent that they considered only the trait-based elements of the Section III approach (i.e., personality functioning [Criterion A] was not considered). As such, similar to Samuel et al. (2013), our reported PD diagnosis rates should not be considered definitive population base rates. Additional work is needed to fully understand how these competing approaches compare when all features and impairment consequences of the Section III approach are considered. Third, although multiple analytical approaches were used, our data were limited self-report responses. Future studies will benefit from considering other assessment methods (e.g., clinical interviews).
In summary, we extended the relatively new literature on the alternative DSM-5 Section III PD classification approach by comparing the Section III trait approach to the traditional criterion-based approach in a psychiatric sample. Although we found general support for traits in predicting traditional PD criteria, problems with trait specificity and overlap emerged at the level of individual PDs. We also found differences in diagnosis rates across systems that carry implications for the broader mental health field. To that end, more research is needed to improve the validity of the Section III trait model, with the end goal of a PD diagnostic system that is empirically supported and compelling to researchers and clinicians.
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Submitted: September 30, 2013 Revised: June 24, 2014 Accepted: July 7, 2014
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Source: Journal of Abnormal Psychology. Vol. 123. (4), Nov, 2014 pp. 802-808)
Accession Number: 2014-32647-001
Digital Object Identifier: 10.1037/a0037633
Record: 14- Title:
- Detecting emotional expression in face-to-face and online breast cancer support groups.
- Authors:
- Liess, Anna. Stanford University School of Medicine, Stanford, CA, US
Simon, Wendy. Stanford University School of Medicine, Stanford, CA, US
Yutsis, Maya. Stanford University School of Medicine, Stanford, CA, US
Owen, Jason E.. Department of Psychology, Loma Linda University, Loma Linda, CA, US
Piemme, Karen Altree. Stanford University School of Medicine, Stanford, CA, US
Golant, Mitch. The Wellness Community-National, Santa Monica, CA, US
Giese-Davis, Janine. Stanford University School of Medicine, Stanford, CA, US, jgiese@stanford.edu - Address:
- Giese-Davis, Janine, Department of Psychiatry and Behavioral Sciences, 401 Quarry Road, Room 2318, MC#5718, Stanford, CA, US, 94305, jgiese@stanford.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 76(3), Jun, 2008. pp. 517-523.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 7
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- emotional expression, text analysis, breast cancer, group therapy, video coding
- Abstract:
- Accurately detecting emotional expression in women with primary breast cancer participating in support groups may be important for therapists and researchers. In 2 small studies (N = 20 and N = 16), the authors examined whether video coding, human text coding, and automated text analysis provided consistent estimates of the level of emotional expression. In Study 1, the authors compared coding from videotapes and text transcripts of face-to-face groups. In Study 2, the authors examined transcripts of online synchronous groups. The authors found that human text coding significantly overestimated Positive Affect and underestimated Defensive/Hostile Affect compared with video coding. They found correlations were low for Positive Affect but moderate for negative affect between Linguistic Inquiry Word Count (LIWC) and video coding. The implications of utilizing text-only detection of emotion are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Breast Neoplasms; *Emotional States; *Emotionality (Personality); *Measurement; *Support Groups; Group Psychotherapy; Internet; Videotapes; Written Communication
- Medical Subject Headings (MeSH):
- Adult; Affect; Aged; Automatic Data Processing; Breast Neoplasms; Facial Expression; Female; Humans; Internet; Middle Aged; Psychotherapy, Group; Signal Detection, Psychological; Social Support; Videotape Recording
- PsycINFO Classification:
- Research Methods & Experimental Design (2260)
Behavioral & Psychological Treatment of Physical Illness (3361) - Population:
- Human
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Thirties (30-39 yrs)
Middle Age (40-64 yrs) - Grant Sponsorship:
- Sponsor: Stanford School of Medicine, US
Other Details: medical scholars project
Recipients: Liess, Anna
Sponsor: California Breast Cancer Research Program, US
Grant Number: 1FB-0383; 4BB-2901; 9IB-0191; 5JB-0102
Recipients: No recipient indicated
Sponsor: Kozmetsky Global Collaboratory
Recipients: No recipient indicated
Sponsor: National Institute on Aging/National Cancer Institute, US
Grant Number: AG18784
Other Details: Program Project, David Spiegel
Recipients: No recipient indicated - Conference:
- The Society of Behavioral Medicine Annual Meeting, 25th, Mar, 2004, Baltimore, MD, US
- Conference Notes:
- Portions of this article were presented at the aforementioned meeting, and at The Society of Behavioral Medicine 23rd Annual Meeting, April 3-6, 2002, in Washington, DC.
- Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Feb 20, 2008; Revised: Feb 5, 2008; First Submitted: May 9, 2007
- Release Date:
- 20080609
- Copyright:
- American Psychological Association. 2008
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/0022-006X.76.3.517
- PMID:
- 18540745
- Accession Number:
- 2008-06469-016
- Number of Citations in Source:
- 17
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2008-06469-016&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2008-06469-016&site=ehost-live">Detecting emotional expression in face-to-face and online breast cancer support groups.</A>
- Database:
- PsycINFO
Detecting Emotional Expression in Face-to-Face and Online Breast Cancer Support Groups
By: Anna Liess
Stanford University School of Medicine
Wendy Simon
Stanford University School of Medicine
Maya Yutsis
Stanford University School of Medicine
Jason E. Owen
Department of Psychology, Loma Linda University
Karen Altree Piemme
Stanford University School of Medicine
Mitch Golant
The Wellness Community—National, Santa Monica, California
Janine Giese-Davis
Stanford University School of Medicine;
Acknowledgement: This research was conducted in partial fulfillment of a medical scholar's project funded through the Stanford School of Medicine to Anna Liess. Additionally part of the research presented was conducted in partial fulfillment of a Stanford undergraduate Honor's Thesis in Biology by Wendy Simon, both under the mentorship of Janine Giese-Davis. The research was supported in part by California Breast Cancer Research Program Grants 1FB-0383, 4BB-2901, and 9IB-0191, 5JB-0102, the Kozmetsky Global Collaboratory, and NIA/NCI Program Project AG18784 to David Spiegel. Portions of this article were presented at The Society of Behavioral Medicine 25th Annual Meeting, March 24–27, 2004, in Baltimore, Maryland, and The Society of Behavioral Medicine 23rd Annual Meeting, April 3–6, 2002, in Washington, DC. We would like to acknowledge Morton Lieberman, principal investigator of the online study; Helena C. Kraemer, biostatistician; coding lab programmers Suzanne Twirbutt and Theo Chakkapark; linguist Alex Gruenstein; coders Kristina Roos and Kinsey McCormick; the rest of the emotion coders at The Emotion Coding Lab—Stanford (Giese-Davis.com); and the women with breast cancer who agreed to be videotaped and who participated in our studies.
Women with breast cancer experience reductions in distress and pain, a decrease in emotional suppression, and increased restraint of hostility after participation in face-to-face (F2F) groups that facilitate emotional expression (Classen et al., 2001; Giese-Davis et al., 2002). They also experience decreased distress, greater heart-rate habituation to writing, and declines in physical symptoms while expressing emotions in written text (Low, Stanton, & Danoff-Burg, 2006). Online synchronous groups (OSGs) for breast cancer combine group support and written text and may similarly decrease distress and pain (Lieberman et al., 2003; Winzelberg et al., 2003) but may lead to increased emotional suppression (Lieberman et al., 2003).
Detecting emotional expression in these groups may be important for therapists and researchers testing whether expression mediates outcomes. Researchers often analyze expression in text transcripts of therapy rather than use video analysis (Low et al., 2006). Likewise, in OSGs, therapists have only text on which to rely for emotional cues. Detecting emotion in text for either would be compromised if some emotion categories rely heavily on non-verbal channels.
Early research on emotion communication channels (i.e., voice, face, text content) indicated that people watching videotapes of social interactions were significantly more accurate (those reading a transcript not exceeding chance levels) when asked interpretive questions (Archer & Akert, 1977). Detecting deceptive negative affect relies on vocal, facial, and text content cues (O'Sullivan, Ekman, Friesen, & Sherer, 1985). However, honest positive affect is difficult to detect in the absence of vocal (Krauss, Appel, Morency, Wenzel, & Winton, 1981), facial, or body cues (O'Sullivan et al., 1985). Since a host of computer-mediated interventions for cancer patients (e-mail, real-time text correspondence, online forums, and electronic support groups; Davison, Pennebaker, & Dickerson, 2000) are now in common practice, we thought it timely to investigate detection of emotional expression comparing human coding of videotape and text transcripts of F2F groups as well as human and text analysis (Pennebaker & Francis, 1999) of F2F groups and OSGs. By using trained human coders and strict reliability standards, we believe we offer the best-case scenario for detection of expression in both videotape and text.
Detecting emotional expression in therapy is a complex process using (a) observational coding from videotape (Giese-Davis, DiMiceli, Sephton, & Spiegel, 2006; Giese-Davis, Piemme, Dillon, & Twirbutt, 2005), (b) coding of transcripts (Grabhorn, 1998), (c) automated text analysis (Low et al., 2006), or (d) content analysis (LaBarge, Von Dras, & Wingbermuehle, 1998). Regardless of method, affect constructs are often labeled with the same words (e.g., positive, negative, or defensive affect) even when assumptions differ dramatically. Trained raters may be in the best position to accurately identify expression in therapy contexts, but instead automated text analysis is increasingly used. The use of these programs assumes that words carry psychological information independent of context (Krippendorf, 2004). Because expression is highly contextual, irony, sarcasm, and anxiety may be missed by text analysis (Davison et al., 2000). Few studies have compared methods. In our two studies, we compare levels of emotion detected from human coding of video versus text (Study 1) in F2F groups and whether text analysis correlates with human coding of similar constructs (Study 1 and 2) in F2F groups and OSGs.
- Hypothesis 1: Differences in levels of affect.
- Due to greater reliance on non-verbal cues, human text coding will underestimate some video-coding categories: validation, high and low affection (included in Positive Affect), high fear, sadness, direct anger (included in Primary Negative Affect), tension, micro-moment contempt, belligerence, disgust, and stonewalling (included in Defensive/Hostile Affect). However, because some categories may be overestimated by text coding, our tests are two-tailed (e.g., interest, which is coded from video when there is a genuine positive voice tone, not just when a woman asks a question). Our study was powered to test summary variables.
- Hypothesis 2: Multimethod–multitrait consistency.
- Though levels of video and text may differ, strong convergent correlations will indicate that coders utilized similar cues. The strength of correlations among categories will replicate within each method (Kenny & Campbell, 1989). These analyses are necessary to examine whether constructs are consistent across methods.
- Exploration: Human and automated text analysis.
- Human coding will correlate significantly with text analysis of similar constructs. These correlations will be larger in OSGs than F2F groups because communication must rely on clarity in text rather than on non-verbal cues.
Method Therapy Model
Both studies were community/research collaborations (March 8, 2000–January 16, 2002) between The Wellness Community (TWC), Stanford University, and the University of California San Francisco (UCSF). In Study 1, we selected women randomized to TWC in a study comparing supportive–expressive (SET) groups with TWC groups. In Study 2, women participated in TWC OSGs (Lieberman et al., 2003).
TWC offered free F2F therapy in Study 1 as part of their ongoing support programs serving over 5,000 cancer patients each week. Groups of 12 participated in weekly, 2-hr groups led by one therapist for 16 weeks. In TWC's “patient-active” model, therapists encourage patients to (a) become empowered; (b) partner with physicians; (c) access resources; (d) make active choices in their recovery; and (e) reduce unwanted aloneness, loss of control, and loss of hope. In Study 2, TWC offered free OSGs where groups of 8 participated in two non-randomized 90-min groups (4 total) led by one therapist for 16 weeks. Women could access a private, 24-hr, TWC-based newsgroup by cohort. The same therapy model was used for both studies.
Participants
Recruitment for both studies was through collaborating community organizations and general advertising, and for Study 2 through online postings on breast-cancer-related sites. Women were eligible if they were over 18 years old, diagnosed with physician-confirmed primary breast cancer (Stages I–III, without metastasis or recurrence), English-literate, less than 18-months posttreatment, and had not attended more than 8 support-group sessions. Women lived in the San Francisco East Bay in Study 1 and in California and throughout the United States in Study 2. Study procedures were approved by institutional review boards at Stanford and UCSF. Participants signed written informed consent and physician contact consents. In Study 1, women could earn up to $40 for completing questionnaires, but no payment was given in Study 2.
In Study 1, of 108 women contacted, 92 consented, 66 completed baseline data, 45 were randomized at the Walnut Creek site (22 to TWC, 23 to SET) and 18 were randomized to a second community site in San Francisco (10 to The Cancer Support Community, 8 to SET). Some attrition occurred due to time delays associated with block randomization. Of 22 women randomized to TWC, 20 attended sessions in two groups. A videographer taped each session, focusing on the woman speaking. In Study 2, women registered at www.twc-chat.org which provided information, consent details, and an invitation to participate. Of 67 women recruited, 35 did not participate due to scheduling difficulties, 32 consented and completed online measures at baseline, and 26 women completed the 16-week measures. We randomly selected 2 of 4 groups (N = 16). The OSG closely mimics F2F group interactions. Demographic and medical variables for both studies are in Table 1. Final sample for Study 1 is N = 20 and for Study 2 is N = 16.
Demographic and Medical Characteristics for Primary Breast Cancer Patients Participating in F2F Breast Cancer Support Groups (N = 20) and in TWC OSGs (N = 16)
Human and Automated Coding
For each study, we coded Sessions 2, 6, 10, and 15 for two groups. For F2F groups, we coded each participant's expression by using Specific Affect (SPAFF) for Breast Cancer (Giese-Davis et al., 2005) for Videotape and for Text (Giese-Davis et al., 2005) following professional transcription. For OSGs, we coded transcripts by using SPAFF for Text. We also conducted Linguistic Inquiry Word Count (LIWC) text analysis (Pennebaker & Francis, 1999) for each participant-by-session segment. We used mean scores across four sessions per woman for analyses.
We used SPAFF for Breast Cancer and SPAFF for Text (Giese-Davis et al., 2005) for human coding and tested hypotheses with Positive Affect (affection, affection with touch, interest, validation, genuine humor, and excitement), Primary Negative Affect (direct anger, low and high sadness, verbalized and high fear), and Defensive/Hostile Affect (tension, tense humor, whining, disgust, micro-moment contempt, verbalized contempt, domineering, and belligerence; Giese-Davis et al., 2005). For video, we coded 1 woman at a time at least twice (68 person-by-tape segments: Mean kappa = 0.70, SD = 0.09). For a kappa of .60 or higher, a coin toss determined which coder's data we used (50 segments). If kappa was below .55, it was recoded (9-by-3 and 3-by-5 coders). Six segments (kappa between .55 and .60) were consensus coded to maintain thresholds. We gave a transcriptionist the timing of speaking turns so that video and text segments were comparable in hours:minutes:seconds:frames. The median correlation between two coders of each F2F transcript was 0.54 (SD = 0.11) for percent time and 0.66 (SD = 0.14) for word count. Either coder's work provided the same magnitude of results. We used consensus coded OSG transcripts because median correlations were 0.58 (SD = 0.27) for percent time but 0.38 (SD = 0.34) for word count. Each separate emotional expression in a stream of data over time has a duration in seconds that we used to calculate percent-time data.
We used LIWC text analysis, which automatically matches each word to 1 or more of 82 language dimensions (Pennebaker & Francis, 1999). Summary scores are the number of words matching a dimension divided by total number of words. Current analysis focused on two dimensions: (a) Expression of Emotion included Positive Emotion (happy), Positive Feeling (joy, love), Optimism (pride, certainty), Assents (yes, OK), Question Marks, Negative Emotion (range of negative words), Sadness (grief, cry), Anger (pissed, hate), Anxiety (nervous, tense, afraid), and Negations (no, not); (b) Cognitive Mechanisms included Inhibition (always, never), Tentativeness (perhaps, might), and Discrepancy (should, would).
Data Analysis
We utilized percentages to assure comparability across methods (F2F groups ran for 2 hr while OSGs ran for 1.5 hr). We utilized percent time to compare video coding (which typically uses mean duration of time; Giese-Davis et al., 2006), with SPAFF text percent time. Time in transcripts was calculated as total time for each utterance divided by number of words. SPAFF text percent word count was used for correlations with LIWC (Table 2).
Type of Coding Measurement by Study Question
In Study 1, to compare methods of human coding, we used the non-parametric Friedman test (due to non-normal distributions) to compare summary variables for three related samples: SPAFF video percent time, SPAFF text percent time, and SPAFF text percent word count (Table 2 and 3; Figure 1). In the analysis of emotion, two measurement traditions exist: one based on duration of affect, one on word count. Because no prior comparisons give an indication of level differences between methods, we compared all three. If significant, we examined three pairwise comparisons with Wilcoxon signed ranks tests. To examine convergent correlations within and between methods, we utilized Spearman correlations (Table 3). We also explored whether the associations among SPAFF categories were consistent whether coded by video or text with a multitrait–multimethod matrix and Kenny and Campbell's method (Campbell & Fiske, 1959; Kenny & Campbell, 1989). If variables within both methods are similar, the same patterns should emerge in all hetero-trait triangles, both within the mono-method triangle (Table 4, italicized numbers) and hetero-method triangle (Table 4, bold numbers). For instance, the size of correlation between variables 1 and 2 in method A ought to be similar to the correlation between variables 1 and 2 in method B. Lastly, we explored correlations among LIWC and SPAFF categories thought to represent similar constructs (Table 5) in F2F groups and OSGs.
Median, 25th, and 75th Percentiles for Specific Affect Summary Codes for Videotape and Text in Women With Primary Breast Cancer (N = 20)
Figure 1. Graphed are box-and-whisker plots for each summary measure of each affect coded for Study 1 from videotape (Specific Affect for Breast Cancer) and transcripts of the videotapes (Specific Affect for Text): video percent time, transcript percent time, and transcript percent word count. Bottom line on whisker = the smallest observation; bottom line on box = lower quartile; middle line on box = median; top line on box = upper quartile; top line on whisker = largest observation; circles = mild outlier; stars = extreme outlier.
Spearman Correlations Among Emotion Categories for Specific Affect for Breast Cancer Video (Percent Time) and Specific Affect for Text (Percent Time)
Spearman Correlations Between SPAFF Video Coding and SPAFF Text Word Count With LIWC Affect Categories
Results Similarity Between Coding Methods in F2F Groups (Table 3, Figure 1)
Significantly more Positive Affect was coded in transcripts than in videotapes. Levels of Primary Negative Affect were generally low and were not different between transcripts and videotape. Significantly less Defensive/Hostile Affect was coded in transcripts than in videotapes. We had no hypotheses about Neutral and Constrained Anger, but we present these for the possible interest of readers.
For Positive Affect, video percent time was significantly lower than both text percent time (z = –3.65, p < .001) and percent word count (z = –2.14, p = .03). Text percent time was significantly higher than percent word count (z = –3.11, p = .002). For Defensive/Hostile Affect, video percent time was significantly higher than either text percent time (z = –3.43, p = .001) or text percent word count (z = –3.81, p < .001). Text percent time and word count did not differ.
Multimethod–Multitrait Consistency
Results indicate that emotion constructs are consistent across methods except for Defensive/Hostile Affect. We found strong convergent validity (Table 4, bold italicized numbers) between methods for video and text percent time: Neutral, Positive Affect, Primary Negative Affect, and Constrained Anger, but not for Defensive/Hostile Affect. We found similar patterns of correlation levels among emotion categories other than those with Defensive/Hostile Affect. For instance, in both the mono-method triangles (Table 4, italicized numbers) for video (emotion categories 1–5) and text coding (emotion categories 6–10), and the hetero-method video- and text-coding block (Table 4, bold numbers), Primary Negative Affect is moderately negatively correlated with Positive Affect, rs = –.24, –.39, and –.27, respectively.
SPAFF and LIWC Correlations (Table 5)
Positive affect variables from LIWC does not correlate significantly with SPAFF Positive Affect coded from video (r = –.34 to .19) or text (r = –.29 to .28) in F2F groups. In OSGs, where participants use emoticons to increase clarity, correlations between SPAFF and LIWC are higher but not significant for Positive Emotion, Positive Feeling, and Questions Marks. There are four negative correlations. Negative affect variables from LIWC correlates moderately with SPAFF F2F video and text Primary Negative Affect (r = .01 to .60, three significant) and Constrained Anger (r = .08 to .74, four significant), indicating that they may measure similar constructs. For negative affect variables in OSGs, SPAFF and LIWC correlations are not significant, and three are negative. LIWC variables thought to be similar correlate moderately with SPAFF F2F video and text Defensive/Hostile Affect (r = –.32 to .46). For Defensive/Hostile Affect in OSGs, SPAFF and LIWC correlations are moderate but not significant.
DiscussionWe compared levels of affect detected by human coders in F2F TWC groups and found that text coding overestimated Positive Affect and underestimated Defensive/Hostile Affect compared with video coding. Differences are likely because text cannot convey intonation, facial expression, and body posturing. We also examined correlations between human coding and automated text analysis in TWC F2F groups and OSGs, finding significant positive correlations for Primary Negative Affect, Constrained Anger, and Defensive/Hostile Affect, but none for Positive Affect.
Our research indicates that genuine positive affect is difficult to judge accurately from text in OSG and F2F groups and supports an earlier finding that accurate assessment of positive affect is more related to voice tone than content (O'Sullivan et al., 1985). We were surprised that significantly more Positive Affect was detected by coders in text than videotape and that text analysis correlated so poorly with human coding. An examination of video and text segments of mismatches indicated that a statement may seem positive on paper but may be interpreted as Defensive/Hostile Affect or Primary Negative Affect in the presence of defensive body posturing, a raised voice, or tears in the eyes. One can only speculate about how the probable increase in perception of positive affect and lack of cues for defensive/hostile affect might affect OSGs.
Non-verbal cues may be crucial for detecting Defensive/Hostile Affect because human coders identified significantly less from text than videotape, and convergent validity was low. If, like our coders, OSG therapists cannot detect hostility accurately, a lasting impact on participants' emotion regulation may be curtailed.
Rates of Primary Negative Affect detected in video and text were similar, and correlations between human coding and text analysis were higher, implying that Primary Negative Affect is conveyed to a greater extent through content. We found equally low levels in both F2F groups and OSGs.
This study was limited by small sample sizes, and the lack of economic and cultural diversity may have restricted the range of affect. Future research could randomize women to F2F groups versus OSGs so that statistical comparison is possible. However, these preliminary studies indicate that video and text methods of detecting emotion yield substantially different results.
We highlight ways in which the interpretation of written text can be changed by non-verbal cues in the following example. A common training technique for SPAFF is to ask the trainee simply to open a book to a random sentence. The trainee reads that sentence with the facial muscle movement, voice tone, and body movement of each one of the 20+ expressions coded by SPAFF. The same words can thus convincingly be used to convey emotional expressions as varied as validation, contempt, domineering, sadness, affection, and joy. Given that written text can be interpreted so broadly, examination of the coherence between methods of coding emotional expression, and caution when interpreting words in text as evidence of a particular emotion, seem crucial.
Based on these results, we recommend that researchers use caution when assessing computer-based and electronic psychotherapy services that depend solely on text. We also recommend that OSG therapists receive training to increase attention to cues for emotion in the absence of voice tone, facial movement, and body movement. OSG therapists may need to frequently double check their perception of affect to counteract their own tendency to see greater positive affect than is warranted (as did our human text coders) in order for these groups to be effective.
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Giese-Davis, J., Piemme, K. A., Dillon, C., & Twirbutt, S. (2005). Macro-variables in affective expression in women with breast cancer participating in support groups. In J.Harrigan, K. R.Scherer, & R.Rosenthal (Eds.), Nonverbal behavior in the affective sciences: A handbook of research methods (pp. 399–445). Oxford, England: Oxford University Press.
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Submitted: May 9, 2007 Revised: February 5, 2008 Accepted: February 20, 2008
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Source: Journal of Consulting and Clinical Psychology. Vol. 76. (3), Jun, 2008 pp. 517-523)
Accession Number: 2008-06469-016
Digital Object Identifier: 10.1037/0022-006X.76.3.517
Record: 15- Title:
- Developmental trajectories of clinically significant attention-deficit/hyperactivity disorder (ADHD) symptoms from grade 3 through 12 in a high-risk sample: Predictors and outcomes.
- Authors:
- Sasser, Tyler R.. Department of Psychology, The Pennsylvania State University, University Park, PA, US, tysasser@gmail.com
Kalvin, Carla B.. of Psychology, The Pennsylvania State University, University Park, PA, US
Bierman, Karen L.. of Psychology, The Pennsylvania State University, University Park, PA, US - Address:
- Sasser, Tyler R., Department of Psychology, The Pennsylvania State University, 140 Moore Building, University Park, PA, US, 16802, tysasser@gmail.com
- Source:
- Journal of Abnormal Psychology, Vol 125(2), Feb, 2016. ADHD Across Development: Risk and Resilience Factors. pp. 207-219.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 13
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- ADHD, aggression, developmental trajectories, adolescent maladjustment
- Abstract (English):
- Developmental trajectories of clinically significant attention-deficit/hyperactivity (ADHD) symptoms were explored in a sample of 413 children identified as high risk because of elevated kindergarten conduct problems. Symptoms of inattention and hyperactivity-impulsivity were modeled simultaneously in a longitudinal latent class analyses, using parent reports collected in Grades 3, 6, 9, and 12. Three developmental trajectories emerged: (1) low levels of inattention and hyperactivity (low), (2) initially high but then declining symptoms (declining), and (3) continuously high symptoms that featured hyperactivity in childhood and early adolescence and inattention in adolescence (high). Multinomial logistic regressions examined child characteristics and family risk factors as predictors of ADHD trajectories. Relative to the low class, children in the high and declining classes displayed similar elevations of inattention and hyperactivity in early childhood. The high class was distinguished from the declining class by higher rates of aggression and hyperactivity at school and emotion dysregulation at home. In contrast, the declining class displayed more social isolation at home and school, relative to the low class. Families of children in both high and declining trajectory classes experienced elevated life stressors, and parents of children in the high class were also more inconsistent in their discipline practices relative to the low class. By late adolescence, children in the high class were significantly more antisocial than those in the low class, with higher rates of arrests, school dropout, and unemployment, whereas children in the declining class did not differ from those in the low trajectory class. The developmental and clinical implications of these findings are discussed. (PsycINFO Database Record (c) 2018 APA, all rights reserved)
- Impact Statement:
- General Scientific Summary—This study supports the notion that clinically significant ADHD symptoms persist into adolescence for some children, but not for others. Children who are more hyperactive or aggressive, or whose parents are inconsistent or ineffective with discipline, are more likely to have clinically significant and stable ADHD symptoms and show more antisocial activities and worse graduation and employment rates in late adolescence. In contrast, children with clinically significant ADHD symptoms who are less hyperactive and aggressive, and who are more socially isolated, tend to show a declining pattern of ADHD symptoms and better functional outcomes. (PsycINFO Database Record (c) 2018 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Adolescent Development; *Aggressive Behavior; *Attention Deficit Disorder with Hyperactivity; *Childhood Development; *Emotional Adjustment; Risk Factors; Symptoms
- Medical Subject Headings (MeSH):
- Adolescent; Adolescent Development; Attention Deficit Disorder with Hyperactivity; Child; Child Development; Female; Humans; Male; Prognosis
- PsycINFO Classification:
- Developmental Disorders & Autism (3250)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Childhood (birth-12 yrs)
School Age (6-12 yrs) - Tests & Measures:
- National Institute of Mental Health’s Diagnostic Interview Schedule for Children
Child Behavior Checklist-Parent Report Form
Child Behavior Checklist-Teacher Report Form
Life Changes Questionnaire
Employment Report Form
Child Behavior Checklist
Parent Questionnaire
Self-Reported Delinquency Scale DOI: 10.1037/t44193-000
Parent Daily Report DOI: 10.1037/t07197-000
Teacher's Report Form DOI: 10.1037/t02066-000
Social Competence Scale DOI: 10.1037/t09698-000 - Grant Sponsorship:
- Sponsor: National Institute of Mental Health
Grant Number: R18 MH48043, R18 MH50951, R18 MH50952, and R18 MH50953
Recipients: No recipient indicated
Sponsor: Center for Substance Abuse Prevention, National Institute on Drug Abuse, National Institute of Mental Health, US
Other Details: also provided support for Fast Track through a memorandum of agreement
Recipients: No recipient indicated
Sponsor: Department of Education
Grant Number: S184U30002
Recipients: No recipient indicated
Sponsor: National Institute of Mental Health
Grant Number: K05MH00797 and K05MH01027
Recipients: No recipient indicated
Sponsor: National Institute on Drug Abuse
Grant Number: DA16903, DA017589, and DA015226
Recipients: No recipient indicated
Sponsor: Institute of Education Sciences
Grant Number: R305B090007
Recipients: Sasser, Tyler R.; Kalvin, Carla B. - Methodology:
- Empirical Study; Longitudinal Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Aug 14, 2015; Revised: Aug 11, 2015; First Submitted: Jan 15, 2015
- Release Date:
- 20160208
- Correction Date:
- 20180212
- Copyright:
- American Psychological Association. 2016
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/abn0000112
- PMID:
- 26854506
- Accession Number:
- 2016-06080-006
- Number of Citations in Source:
- 59
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2016-06080-006&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2016-06080-006&site=ehost-live">Developmental trajectories of clinically significant attention-deficit/hyperactivity disorder (ADHD) symptoms from grade 3 through 12 in a high-risk sample: Predictors and outcomes.</A>
- Database:
- PsycINFO
Developmental Trajectories of Clinically Significant Attention-Deficit/Hyperactivity Disorder (ADHD) Symptoms From Grade 3 Through 12 in a High-Risk Sample: Predictors and Outcomes
By: Tyler R. Sasser
Department of Psychology, The Pennsylvania State University;
Carla B. Kalvin
Department of Psychology, The Pennsylvania State University
Karen L. Bierman
Department of Psychology, The Pennsylvania State University
Acknowledgement: This work was supported by National Institute of Mental Health (NIMH) Grants R18 MH48043, R18 MH50951, R18 MH50952, and R18 MH50953. The Center for Substance Abuse Prevention and the National Institute on Drug Abuse also provided support for Fast Track through a memorandum of agreement with the NIMH. This work was also supported in part by Department of Education Grant S184U30002, NIMH Grants K05MH00797 and K05MH01027, and National Institute on Drug Abuse (NIDA) Grants DA16903, DA017589, and DA015226. The first two authors were supported by Grant R305B090007 from the Institute of Education Sciences.
We thank the Fast Track project staff and participants and acknowledge the critical contributions and support of the Conduct Problems Prevention Research Group members John Coie, Kenneth Dodge, Mark Greenberg, John Lochman, Robert McMahon, and Ellen Pinderhughes. The views expressed in this article are ours and do not necessarily represent the granting agencies.
Note: Steve S. Lee served as the Guest Editor for this article. —SHG
Attention-deficit/hyperactivity disorder (ADHD) is considered a biologically based but heterogeneous disorder associated with an array of negative outcomes (Barkley, 2006). Although typically conceptualized as chronic, longitudinal research indicates continuity and discontinuity in the course of ADHD (Lahey, Pelham, Loney, Lee, & Willcutt, 2005; Willoughby, 2003). In general, longitudinal studies suggest that inattention persists, whereas hyperactivity-impulsivity (referred to as hyperactivity in the remainder of the article) declines with age (Biederman, Mick, & Faraone, 2000). However, more recent person-oriented analyses suggest more nuanced developmental trajectories (Arnold et al., 2014; Larsson, Dilshad, Lichtenstein, & Barker, 2011). Adding to prior trajectory research, this study modeled inattention and hyperactivity simultaneously to better understand the longitudinal covariation of clinically significant ADHD symptoms across developmental periods (from elementary school through late adolescence). The primary goal of this study was to examine conceptually relevant early child characteristics and family adversity factors that might differentiate children in the developmental trajectories. In addition, late adolescent functioning was explored to enhance understanding of the developmental outcomes associated with the trajectories.
ADHD TrajectoriesA growing body of research highlights important heterogeneity in the developmental course of ADHD. For example, although a majority of children diagnosed with ADHD show chronic difficulties, 20%–50% of children with ADHD no longer meet diagnostic criteria as they move through adolescence, suggesting remission, or at least a marked reduction in symptom severity for some children (Barkley, 2006; Biederman et al., 1996). In recent years, person-oriented analyses have been applied to track the course of ADHD. Studies modeling parent-reported ADHD (inattention and hyperactivity combined) from childhood into mid-adolescence generally document stability over time, revealing chronically elevated ADHD in one (van Lier, van der Ende, Koot, & Verhulst, 2007) or two classes (Malone, Van Eck, Flory, & Lamis, 2010). It is interesting, however, that studies modeling teacher-rated inattention alone reveal more developmental variability, including high, low, increasing, and decreasing trajectories (Pingault et al., 2011; Sasser, Beekman, & Bierman, 2014). Modeling inattention in the absence of hyperactivity, however, does not adequately capture ADHD development. For example, Greven, Asherson, Rijsdijk, and Plomin (2011) found that, despite declines in hyperactivity over time, childhood hyperactivity predicted increased adolescent inattention (controlling for early childhood inattention).
Two studies have compared trajectories of parent-rated inattention and hyperactivity to better understand symptom covariation across time. Following a normative sample across the ages of 9–17, Larsson et al. (2011) found that many children in a stable-high inattention trajectory were also in a declining hyperactivity trajectory, suggesting a “shift” from childhood inattention-hyperactivity to adolescent inattention. Similarly, in children at risk for bipolar disorder from ages 6–12, Arnold et al. (2014) found that, in addition to profiles that were stable (high or low on inattention and hyperactivity), another profile demonstrated decreasing hyperactivity but stable high inattention. These studies suggest that developmental patterns of ADHD might be best understood by allowing for covariation between inattention and hyperactivity. In the present study, longitudinal latent class analysis (LLCA; Collins & Lanza, 2010) permitted for the simultaneous inclusion of clinically significant inattention and hyperactivity symptoms in the same longitudinal model, an enhancement over prior studies that compared separate symptom trajectories. The major focus of this study was to examine child and family risk factors that might differentiate diverging ADHD trajectory patterns (e.g., chronically high vs. declining).
Predicting ADHD Trajectories: Child Characteristics and Family AdversityRecognizing the centrality of cognitive and behavioral self-regulation deficits, models of ADHD development suggest that dysfunction in biologically based regulatory systems precedes ADHD and influences its stability (Barkley, 2006; Campbell, Halperin, & Sonuga-Barke, 2014). Developmental models also recognize that socialization experiences may affect the development of self-regulatory capacities and compensatory skills, thereby altering the course and outcomes of ADHD (Campbell et al., 2014). In particular, high-quality socialization experiences, including positive adult–child interactions and peer relations, appear to facilitate the development of child attention, emotion, and behavior regulation skills (Bernier, Carlson, Deschênes, & Matte-Gagné, 2012; Bierman & Torres, in press). Conversely, inconsistency, nonresponsiveness, or hostility in the socializing environment may impair self-regulatory control and exacerbate child reactivity and impulsivity (Cicchetti, 2002).
It is interesting that a recent review of prospective longitudinal studies of children with ADHD identified risk factors that appear particularly salient in predicting the course of ADHD; among them were the severity of inattention and hyperactivity, concurrent aggression, social isolation, emotional difficulties, and family adversity (Cherkasova, Sulla, Dalena, Pondé, & Hechtman, 2013). These factors may be linked directly with the course of ADHD to the extent that they index dysfunction in biologically based regulatory systems associated with ADHD (Barkley, 2006). In addition, they may affect the developmental course of ADHD indirectly, by increasing or decreasing child exposure to the types of predictable and supportive socialization experiences associated with the development of self-control capacities (Campbell et al., 2014). Evidence supporting the potential influence of each factor is considered briefly in the following sections.
Severity of inattention and hyperactivity
Reflecting the degree of cognitive and behavioral dysfunction, the severity of inattention and hyperactivity in childhood predicts ADHD in adolescence (Cherkasova et al., 2013). More severe inattention undermines school performance and effective social interaction, reducing positive support from teachers and peers (Campbell et al., 2014). Elevated hyperactivity is associated with disruptive and rule-breaking behaviors that increase negative exchanges with parents, teachers, and peers, thereby further fueling emotional reactivity and social alienation (Beauchaine, Hinshaw, & Pang, 2010; Campbell et al., 2014). Among children with ADHD, symptom severity may thus affect the course of the disorder by increasing risk for negative socialization experiences and reducing the positive supports that foster the continued development of self-regulation skills.
Aggression and Social Isolation
An extensive database suggests that comorbid aggression increases the stability of childhood ADHD (Hawes, Dadds, Frost, & Russell, 2013). In addition, aggression has been linked to stable high or increasing trajectories of ADHD relative to low trajectories (Arnold et al., 2014; Sasser et al., 2014; Todd et al., 2008). In the early school years, elevated aggression may reflect heightened temperamental reactivity, serving as a direct index of biologically based liabilities (Vitaro, Brendgen, & Tremblay, 2002). In addition, aggressive behavior greatly increases exposure to coercive exchanges in which peers and adults escalate and reinforce aggressive and impulsive behaviors, undermining the development of self-control (Bierman & Sasser, 2014; Vitaro et al., 2002).
Social isolation is also linked with ADHD, particularly inattention (Willcutt et al., 2012), leading some to suggest that cognitive and temperamental characteristics (low inhibitory or effortful control, low social approach) accrue in some children to yield a pattern of general disengagement (Milich, Balentine, & Lynam, 2001). Children who are disengaged cognitively and socially miss out on key developmental opportunities during the school years, including academic instruction and positive interactions with teachers and peers (Campbell et al., 2014). In consequence, socially isolated children with ADHD may be less likely than socially integrated children to develop competencies that might mitigate their difficulties in later years.
Emotional Difficulties
Characterized behaviorally by irritability and emotional outbursts, emotion dysregulation has received increasing focus as a key factor in the development of ADHD (Shaw, Stringaris, Nigg, & Leibenluft, 2014). Conceptually, by the early school years, elevated emotion dysregulation reflects high levels of temperamental reactivity and negative transactions with caregivers, resulting in difficulty managing strong feelings and coping effectively with frustration or disappointment (Beauchaine, Gatzke-Kopp, & Mead, 2007). Research suggests that children with ADHD and emotion dysregulation are more likely to experience social impairment and more persistent ADHD 4 years later relative to children with ADHD only (Biederman et al., 2012), perhaps as a function of both direct influences and insufficient socialization.
Nearly a quarter of the children with ADHD also express emotional distress, including anxiety and depressed mood (Jarrett & Ollendick, 2008). It has been hypothesized that anxiety or depression may exacerbate problems associated with ADHD by compounding cognitive with emotional difficulties (e.g., Bubier & Drabick, 2009). That said, longitudinal studies have yielded mixed results. Whereas emotional distress (mood or anxiety disorders) differentiated boys with persistent ADHD from those with symptom remission in one study (Biederman et al., 1996), it did not differentiate ADHD trajectories in another (Arnold et al., 2014).
Family Adversity
In addition to child factors, family adversity has been implicated in the course of ADHD (Biederman, Faraone, & Monuteaux, 2002; Counts, Nigg, Stawicki, Rappley, & von Eye, 2005). For example, high and low ADHD trajectories are differentiated by low socioeconomic status (SES), large family size, and single-parent status (Galéra et al., 2011; Larsson et al., 2011; Sasser et al., 2014). Theoretically, exposure to family adversity may maintain or exacerbate ADHD symptoms because of heightened stress and reduced support that directly undermine the development and functioning of self-regulatory systems (Bernier et al., 2012; Cicchetti, 2002). In addition, family adversity may impair parenting and increase negative parent–child interactions. For example, Galéra et al. (2011) found that coercive parenting differentiated children in low versus high ADHD trajectory groups, and Hawes et al. (2013) linked inconsistent parenting with increased ADHD symptoms 1 year later. Together, these studies suggest that low SES, single-parent status, exposure to stressful life events, and ineffective parenting may contribute to chronically high ADHD trajectories.
Validating ADHD Trajectories: Evidence of Differential OutcomesA significant limitation in the earlier literature is a lack of studies examining the link between different developmental patterns of ADHD and later youth outcomes (Pingault et al., 2014; Willoughby, 2003). In general, ADHD significantly increases risk for maladjustment in late adolescence and adulthood, including antisocial activities, school failure, and unemployment (Barkley, 2006). However, only a few studies have validated changes in ADHD by examining developmental outcomes. It is possible that children may show declining patterns of ADHD without necessarily reducing their risk for negative outcomes. For example, Pingault et al. (2011) and Sasser et al. (2014) both found that children with high levels of inattention at school entry experienced significant academic difficulties in the later elementary years, even if their symptoms declined, perhaps because inattention during the early school years impeded acquisition of basic academic skills key for later learning. Links between ADHD trajectories and adolescent antisocial activities or adaptation difficulties (high school dropout, unemployment) are understudied. The current study added to this important database.
Present StudyIn summary, the current study had three research aims. First, longitudinal patterns of clinically significant inattention and hyperactivity were estimated simultaneously using parent ratings collected in Grades 3, 6, 9, and 12. Consistent with prior studies that modeled parallel trajectories of inattention and hyperactivity (Arnold et al., 2014; Larsson et al., 2011), it was anticipated that profiles reflecting stable high and low ADHD symptoms would emerge, as well as profile(s) that reflected discontinuity in inattention and/or hyperactivity. Second, child characteristics (inattention, hyperactivity, aggression, social isolation, emotion dysregulation, and emotional distress) and family adversity (low SES, single-parent status, life stress, inconsistent parenting) were explored as predictors of ADHD trajectories. Predictors were measured in the early school years (kindergarten to Grade 2), when children faced new demands for self-regulation, social interaction, and learning, thereby providing an index of functioning in both home and school contexts (Campbell & Von Stauffenberg, 2008). It was anticipated that elevations in child and family risk factors would be associated with more chronic ADHD profiles. Finally, ADHD trajectories were examined in relation to late adolescent outcomes (antisocial activities, high school dropout, unemployment). It was expected that children with more chronic profiles of ADHD would experience more impairment in late adolescence.
Method Participants
This study included participants of the Fast Track project, a multisite, longitudinal study of children at risk for conduct problems. Children were recruited from 55 schools serving high-risk communities located within four sites (Durham, NC; Nashville, TN; Seattle, WA; and rural PA). Using a multiple-gating screening procedure, all 9,594 kindergarteners across three cohorts (1991–993) were screened for classroom conduct problems by teachers (TOCA-R Authority Acceptance; Werthamer-Larsson, Kellam, & Wheeler, 1991). Children scoring in the top 40% within cohort and site were then screened for home behavior problems by parents, using items from the Child Behavior Checklist (Achenbach, 1991) and similar scales (91% of those eligible participated, n = 3,274). Teacher and parent screening scores were standardized and summed to yield a total severity-of-risk screen score, and children were selected for inclusion into the study based on this screen score, moving from the highest score downward. Deviations were made when a child failed to matriculate in the first grade at a core school (n = 59) or refused to participate (n = 75). The outcome was that 891 high-risk children (ns = 445 for intervention and 446 for control) participated in the Fast Track project. On the kindergarten Teacher’s Report Form of the Child Behavior Checklist (TRF), which provides national norms, the average Externalizing T score (available for 88% of the sample) was 66.4, and 76% of these children scored in the subclinical or clinical range (T scores of 60 or higher). The sample used in this study included participants from the high-risk control group (48% African American, 49% European American, 3% other; 66% male) who did not receive any prevention services. At the first home assessment (end of kindergarten) they were on average 6.5 years (SD = 0.48 years).
Developmental trajectories of clinically significant ADHD symptoms were estimated for 413 children (93% of the high-risk control sample) who had parent ratings of ADHD from at least one assessment (Grades 3, 6, 9, and 12). During trajectory estimation using LLCA, missing data was handled using full information maximum likelihood technique (FIML; Lanza, Dziak, Huang, Wagner, & Collins, 2014). This allowed the inclusion of children who had parent ratings at all four time points (50%), three time points (24%), two time points (11%), or one time point (8%). The 33 children dropped from the study because they lacked parent ratings did not differ significantly from those included on any child or family characteristics studied here. Missing data in the outcome variables (ranging from 14–39% of the sample) was multiply imputed.
Procedures
Parents were interviewed annually at home in the summers by trained research staff. Parents provided informed consent at each time. In the spring of the early elementary years (kindergarten, Grades 1 and 2), research assistants delivered and explained measures to teachers, who completed and returned them to the project. During summer home visits following Grade 12, youth completed computer-administered interviews in which they listened to questions via headphones and responded directly on the computer. Teachers, parents, and youth received financial compensation for study participation. All study procedures complied with American Psychological Association (APA) ethical standards and were approved by the institutional review boards of the participating universities.
Measures
Measures used in the current study are described here, with greater details available at http://www.fasttrackproject.org/data-instruments.php.
ADHD
When children were in Grades 3, 6, 9, and 12, parents completed the computerized version of the National Institute of Mental Health’s Diagnostic Interview Schedule for Children (CDISC; Shaffer & Fisher, 1997), a structured interview designed to assess psychiatric disorders and symptoms defined by the DSM–III–R (for Grade 3; American Psychiatric Association [APA], 1987) or DSM–IV (for Grades 6, 9, and 12; APA, 2000). For the ADHD diagnosis module, the parent responded “yes” or “no” to indicate the presence of each of nine inattention and nine hyperactivity symptoms in the prior 6 months (for Grade 3) or prior year (for Grades 6, 9, and 12). To estimate trajectories of clinically significant ADHD symptoms, inattention and hyperactivity were each scored dichotomously, with the presence of six or more symptoms (in Grades 3, 6, and 9) or 5 or more symptoms (in Grade 12; APA, 2013) scored “1” to indicate severity reaching clinically significant levels or “0” if below that threshold.
Early child characteristics
In the early school years (kindergarten to Grade 2), inattention, hyperactivity, aggression, social isolation, and emotional distress were assessed with the Child Behavior Checklist-Parent Report Form (CBCL-PRF; Achenbach, 1991) and Child Behavior Checklist-Teacher Report Form (CBCL-TRF). Scale scores of inattention, hyperactivity, and aggression were based on narrow-band scales previously validated by the Fast Track project (Stormshak, Bierman, & Conduct Problems Prevention Research Group, 1998). Fifteen items assessed inattention, including cannot finish things, cannot concentrate, inattentive, and does not finish tasks (average α = .66 for parents, α = .95 for teachers). Thirteen items assessed hyperactivity, including hyperactive, fidgets, disturbs others, impulsive, talks out of turn (average α = .75 for parents, α = .95 for teachers). Nine items assessed aggression, including gets in many fights, physically attacks people, threatens, and cruel (average α = .70 for parents, α = .81 for teachers). Social isolation was assessed using a 9-item CBCL narrow-band scale, including prefers to be alone, shy, and withdrawn (average α = .70 for parents, average α = .81 for teachers). Emotional distress was assessed with the anxiety and depression CBCL narrow-band scale, including 14 items, such as lonely, cries, feels worthless, self-conscious, unhappy, and worries (average α = .81 for parents, α = .84 for teachers). Each CBCL item was rated on a 3-point scale (0 = not true, 1 = somewhat/sometimes true, 2 = very/often true). Raw scores were averaged across the three years within rater and divided by the number of items in the scale to represent average item ratings. Emotion dysregulation was assessed with the Emotion Regulation subscale of the Social Competence Scale (Conduct Problems Prevention Research Group, 1995), which included 6 items for parents and 10 items for teachers (e.g., accepts things not going his or her way, copes well with failure, controls temper in a disagreement, appropriately expresses needs and feelings). Each item was rated on a 5-point scale (from 0 = not at all to 4 = very well; average α = .85 for parents, α = .97 for teachers). Scores were reversed to reflect emotion dysregulation and averaged across the 3 years.
Early family adversity
In kindergarten to Grade 2, parents reported on their occupation and educational level, which were scored using Hollingshead’s (1975) system to create 5 levels of SES ranging from 1 = professional/major business to 5 = unskilled labor/service worker. In two-parent families, the codes for SES for each parent were averaged each year, and scores across the 3 years were averaged to reflect family SES. Parents reported on marital status (0 = married, 1 = single parent–separated/divorced, widowed, or never married).
During the interviews, parents completed the Life Stress scale of the Life Changes Questionnaire (Dodge, Bates, & Pettit, 1990), which included 16 items describing stressful life events during the past year (e.g., medical problems with target child, medical problems with family, separation of target child’s parents, financial problems, legal problems, pregnancies). Items represented a selection of common stressors represented on life event checklists (Dohrenwend, 2006), and were rated on a 3-point scale (0 = did not occur, 1 = minor stressor, 2 = major stressor). Scores were averaged across the 3 years (average α = .61). Parents also reported on discipline strategies using the Consistent Discipline subscale of the Parent Questionnaire (Strayhorn & Weidman, 1988). Seven items were rated on a 4-point scale (0 = never to 4 = all the time) to describe consistency and follow-through in limit-setting (e.g., When you give your child a command or order to do something, what fraction of the time do you make sure that your child does it? How often do you think that the kind of punishment you give your child depends on your mood?). Scores were reversed to reflect inappropriate and inconsistent discipline and were averaged across the 3 years (average α = .71).
Late adolescent outcomes
At the end of Grade 12, parents completed the Parent Daily Report (Chamberlain & Reid, 1987), which included an 8-item assessment of antisocial behavior (e.g., physically fight with anyone, tell a lie, take anything that didn’t belong to him/her, purposely destroy property, scream/yell/or shout at anyone, argue or talk back to an adult; α = .73). Youth completed the Self-Reported Delinquency scale (Hawkins, Catalano, & Miller, 1992), responding yes/no to describe delinquent behavior during the past year (e.g., property damage, theft, assault; α = .87). Juvenile arrest data was collected from the court system in the child’s county of residence and surrounding counties through Grade 12. Records included any crime for which the individual had been arrested and adjudicated, with the exception of probation violations or referrals to youth diversion programs for first time offenders. Arrests were categorized into five severity levels, ranging from 1 = status or traffic offenses (e.g., curfew violation, runaway, truancy) to 5 = violent crimes that involve serious harm to others (e.g., aggravated robbery or assault, murder, rape). A “lifetime severity weighted frequency of arrests” index was used in the current study reflecting both the number and severity of offenses for which an individual had been arrested through Grade 12 (Cernkovich & Giordano, 2001).
High school noncompletion was recorded if school records did not indicate a diploma within two years after a nonretained student would have completed Grade 12, and the youth had not passed a high school graduation equivalency test (GED). If school records were missing, participant and parent interviews were used to assess high school graduation. Youth reported on employment status using the Employment Report Form (ERF; Howe & Frazis, 1992) at 2 years after Grade 12. Employment status in the present study was categorized into three levels (0 = full time job, 1 = part time job, 2 = unemployed), with higher scores reflecting unemployment.
Results Analysis Plan
Analyses proceeded in three steps. First, parent ratings of clinically significant ADHD symptoms at Grades 3, 6, 9, and 12 were submitted to LLCA, a mixture model approach for identifying trajectory classes based on categorical observed indicators (Collins & Lanza, 2010). Second, a classify/analyze approach was used to assign children to the best trajectory class and multinomial logistic regression analyses examined early elementary child characteristics and family adversity as predictors of trajectory membership. Finally, ANCOVAs compared the late adolescent outcomes of children in different trajectories.
Descriptive Statistics
Rates of clinically significant levels of hyperactivity were 22.2% (Grade 3), 10.6% (Grade 6), 5.3% (Grade 9), and 5.6% (Grade 12). Rates of clinically significant levels of inattention were 19.6% (Grade 3), 16.5% (Grade 6), 15.8% (Grade 9), and 10.7% (Grade 12). Descriptive statistics for other study variables are shown in Table 1. In early elementary school, rates of child difficulties and family adversity were elevated in this high-risk sample. Teachers rated children as more impaired on each child characteristic than did parents, with the exception of emotional distress. In addition, low SES, high rates of single parenthood (more than half of the sample), and elevated levels of inconsistent parenting and life stress characterized the sample.
Descriptive Statistics for Study Variables
Significant sex and demographic (urban African American, urban European American, and rural European American) differences (p < .05) emerged for several study variables. Boys received higher scores than girls on inattention, Fteachers (1, 445) = 4.67; hyperactivity, Fteachers (1, 445) = 20.77; aggression, Fteachers (1, 445) = 33.06, Fparents (1, 445) = 5.49,; emotion dysregulation, Fteachers (1, 444) = 9.81, Fparents (1, 445) = 3.96; and were more likely to live in single-parent families, F(1, 435) = 4.57. In late adolescence, boys reported higher levels of delinquency, F(1, 407) = 9.77, and higher rates of juvenile arrest, F(1, 407) = 17.42, and school dropout, F(1, 407) = 6.26. Urban African American children received higher scores than urban or rural European American children on teacher-rated inattention, F(2, 442) = 12.81; hyperactivity, F(2, 442) = 23.66; aggression, F(2, 442) = 23.07; social isolation, F(2, 442) = 7.46; emotion dysregulation, F(2, 441) = 25.90; and emotional distress, F(2, 436) = 12.62; and were more likely to live in single-parent families, F(2, 432) = 47.63. In late adolescence, urban African American children had higher rates of juvenile arrests, F(2, 407) = 7.83; high school noncompletion, F(2, 407) = 3.79; and unemployment, F(2, 407) = 5.12. Rural European American children received higher scores on parent-rated emotion dysregulation, F(2, 407) = 3.56, and emotional distress, F(2, 407) = 2.98, than the other demographic groups, and urban and rural European American children experienced elevated levels of life stress relative to urban African American children, F(2, 407) = 8.69. Sex and demographic groups were included as covariates in all subsequent analyses. Correlations among early child characteristics and family adversity are shown in Table 2, among late adolescent outcomes in Table 3, and between early child and family characteristics and emerging adult outcomes in Table 4.
Correlations Among Early Child Characteristics and Family Adversity (Grades K–2)
Correlations Among Late Adolescent Outcomes
Correlations of Early Child and Family Factors With Late Adolescent Outcomes
LLCA
The first step of the analyses was to characterize developmental trajectories, applying PROC LCA Version 1.3.1 (Lanza et al., 2014) parent reports of inattention and hyperactivity, dichotomized at clinically significant thresholds (Grades 3, 6, 9, and 12). To select the appropriate number of trajectory classes, 1,000 iterations of each model were run using randomly generated starting values. Adequate model fit (indicated by a G2 statistic less than the degrees of freedom), and lower levels of the Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and adjusted BIC, along with model interpretability, were used to identify the optimal number of classes (Collins & Lanza, 2010). Parameters for LLCA models including 1–6 trajectory classes are shown in Table 5. Models with two (lowest BIC), three (lowest adjusted BIC), and four trajectory classes (lowest AIC) had adequate fit. However, the difference between the AICs of the three and four trajectory class solutions was negligible, and therefore the more parsimonious three trajectory class model was favored as the final LLCA model.
Longitudinal Latent Class Model Parameters
Item-response probabilities are shown in Table 6 and illustrated in Figure 1. A low trajectory class (consistently low levels of inattention and hyperactivity) included 71% of the sample, and a declining trajectory (clinically significant inattention and hyperactivity in third grade, declining below clinical levels in adolescence) included 16%. A third trajectory class (labeled high) included 13% of the sample and was characterized by a high probability of clinically significant hyperactivity in Grade 3, inattention and hyperactivity in Grade 6, and inattention in Grades 9 and 12. As shown in Table 4, there were no statistically significant demographic differences associated with trajectory class membership, with statistically equivalent proportions of males and females, urban African American youth, urban European American youth, and rural European American youth represented in each longitudinal profile.
Longitudinal Latent Classes: Item Response Probabilities and Demographics
Figure 1. Item-response probabilities for longitudinal three-class model of attention-deficit/hyperactivity disorder (ADHD) symptoms.
A classify-analyze approach was used to assign each child to the LLCA trajectory class in which he or she had the highest posterior probability (Lanza et al., 2014). Average posterior probabilities were fairly high (0.95, 0.84, and 0.85 for the low, declining, and high class, respectively) and the proportions of children assigned to each group corresponded closely with the prevalence estimates in the LLCA model, indicating little classification error.
Early Child Characteristics and Family Adversity as Predictors of ADHD Trajectories
To identify factors that differentiated the trajectories, multinomial logistic regression models were estimated for each early elementary child characteristic and family risk. Type III tests provided an omnibus assessment of the contribution of each risk factor (controlling for child sex and site/race); odds ratios (ORs) provided pairwise comparisons of the effect of each risk factor for each trajectory versus the others. First, as shown in the left column of Table 7, relative to those in the low ADHD trajectory, children in the high trajectory were more likely to be rated by teachers and parents as inattentive (ORs = 1.53 and 2.16), hyperactive (ORs = 3.06 and 3.12), aggressive (ORs = 2.24 and 1.82), emotionally dysregulated (ORs = 3.44 and 2.51), and emotional distressed (ORs = 1.53 and 1.52). In fact, only one child characteristic (social isolation) did not differentiate the high and low trajectories. Regarding family adversity, children in the high ADHD trajectory were also more likely to experience greater life stress (OR = 1.44) and more inconsistent parenting (OR = 1.41) than children in the low trajectory.
Regressions Comparing Attention-Deficit/Hyperactivity Disorder (ADHD) Trajectories on Child Characteristics and Family Adversity
Second, as shown in the middle column of Table 7, relative to children in the low ADHD trajectory, children in the declining trajectory were more likely to be rated by parents and teachers as more inattentive (ORs = 1.88 and 2.27), hyperactive (ORs = 1.71 and 2.22), emotionally dysregulated (ORs = 2.17 and 1.54), and socially isolated (ORs = 1.32 and 1.37). It is interesting that only parents (but not teachers) rated children in the declining trajectory as more aggressive (OR = 1.50) and emotionally distressed (OR = 1.45) than children in the low trajectory. Regarding family adversity, children in the declining ADHD trajectory were more likely to experience life stress (OR = 1.69) than children in the low trajectory.
Third, as presented in the right column of Table 7, relative to children in the declining ADHD trajectory, children in the high trajectory were more likely to be rated by teachers (but not parents) as hyperactive (OR = 1.79) and aggressive (OR = 1.90). In addition, parents reported that children in the high trajectory were more emotionally dysregulated (OR = 1.63).
Late Adolescent/Early Adult Outcomes Associated With ADHD Trajectories
To examine group differences in late adolescent outcomes, analyses of covariance (ANCOVAs) controlling for sex, race/site, and early parent-rated aggression were conducted. Results, presented in Table 8, revealed omnibus differences on each of the outcomes assessed. Consistent with expectations, children in the low trajectory demonstrated the best outcomes in late adolescence. Post hoc pairwise comparisons revealed that children in the high ADHD trajectory had significantly higher levels of antisocial behavior (by parent and self-report), arrests, and unemployment compared with children in the low trajectory. Children in the declining and low trajectories did not differ on any late adolescent outcomes. Children in the high trajectory had significantly greater levels of antisocial behavior (by parent report) and higher rates of school dropout than children in the declining group, but the two groups did not differ on self-reported antisocial behavior or unemployment, or juvenile arrests.
Analyses of Covariance (ANCOVAs) Comparing Attention-Deficit/Hyperactivity Disorder Trajectories on Late Adolescent/Early Adult Outcomes
DiscussionAlthough ADHD is often considered a chronic disorder, emerging longitudinal research suggests variability in its developmental course. In this study, LLCA methods identified three developmental trajectories of inattention and hyperactivity (modeled simultaneously) in a high-risk sample of children screened for early conduct problems. Overall, 71% of the sample showed a low trajectory, with no clinically significant levels of inattention or hyperactivity across Grades 3 to 12. The other 29% exhibited clinically significant ADHD symptoms at one or more points in time. This rate is higher than the level of parent-reported ADHD symptoms in normative populations (around 8.8%, Willcutt, 2012), reflecting the high-risk status of this sample. Of these, 16% showed a declining trajectory, with clinically significant levels of inattention and hyperactivity symptoms in Grade 3, declining below clinical levels in late childhood and adolescence (Grades 6, 9, and 12). The other 13% of the sample fell into a high trajectory class characterized by clinical levels of hyperactivity symptoms in Grade 3, inattention and hyperactivity symptoms in Grade 6, and inattention in Grades 9 and 12. A major study goal was to better understand the early elementary risk factors that predicted ADHD symptom trajectories, and which therefore might serve as viable targets for intervention.
The High Trajectory Class
Across studies that have used person-oriented analyses to examine trajectories of ADHD symptoms, many find a profile that is characterized by relatively high, stable symptom levels, reflecting ADHD as a chronic disorder. In the current study, with inattention and hyperactivity symptoms included in the same LLCA, the high trajectory class was characterized by clinically significant levels of hyperactivity during childhood and early adolescence, which declined below the clinically significant threshold in late adolescence. In contrast, inattention reached clinically significant levels in early adolescence and dominated symptom expression in later adolescence. These developmental trends are consistent with findings from prior studies of ADHD symptoms, with hyperactivity declining somewhat over time and inattention remaining relatively stable (Biederman et al., 2000; Willcutt et al., 2012). The pattern found here is also consistent with the findings of Larsson et al. (2011), who compared separate models of inattention and hyperactivity trajectories and suggested that elevated hyperactivity symptoms in childhood are associated with elevated inattention in adolescence. Some researchers have speculated that hyperactivity becomes increasingly internalized with age, manifesting as mental restlessness and distractibility in adolescence (Greven et al., 2011; Weyandt et al., 2003). It is also possible that delays in attention become more pronounced over time as the gap between executive function skill development and increased task demands widens with age (Huizinga, Dolan, & van der Molen, 2006; Willcutt et al., 2012).
In this study, children in the high trajectory class were distinguished from children without clinically significant ADHD symptoms on a host of early childhood characteristics, including elevated inattention and hyperactivity, aggression, emotion dysregulation, and emotional distress. Their parents reported heightened levels of family stress and difficulties with inconsistent and ineffective discipline in the early school years. Children in this high trajectory class had poorer outcomes, including higher rates of antisocial behavior, juvenile arrests, and unemployment than children in the low class, even after controlling for childhood aggression. Considered together, these predictors, trajectories, and outcomes are consistent with negative cascade models of ADHD, in which initial biologically based (i.e., temperamental, cognitive) reactivity and dysregulation contribute to impulsive behaviors and difficulty following rules and routines, as well as a tendency to respond to limit-setting with oppositional or aggressive behavior (Campbell et al., 2014). These early difficulties are exacerbated by inconsistent and ineffective parenting and a lack of positive interpersonal support, which undermine the further development of self-regulation capacities, contribute to poor school adjustment and underachievement, and reinforce antisocial activities (Bierman & Sasser, 2014; Campbell et al., 2014).
The Declining Trajectory Class
Declining trajectory class characteristics
In contrast to the high trajectory class, slightly more than half of the children with elevated ADHD symptoms in childhood (Grade 3) followed a declining trajectory in which their symptoms fell below clinical cut-offs for each of the subsequent time periods. Previous studies modeling inattention symptoms alone have also found declining trajectories (e.g., Pingault et al., 2011; Sasser et al., 2014). The current study revealed a trajectory class characterized by declines in both inattention and hyperactivity. Relative to children in the low trajectory who never exhibited elevated ADHD symptoms, children in the declining trajectory class showed multiple difficulties during the initial school years (kindergarten to Grade 2), including elevated inattention, hyperactivity, social isolation, and emotion dysregulation by both teacher and parent report (relative to the low group). Their parents also reported elevated aggression at home, and elevated levels of life stressors, which reflect events and experiences that undermined family support, such as moves, job changes, interpersonal losses, and medical problems. By late adolescence, not only were their ADHD symptoms improved, but these children fared better than those in the high trajectory class in areas of parent-reported antisocial outcomes and rates of high school completion. Although they were not significantly different from youth in the low ADHD trajectory on any of the late adolescent outcomes studied here, they had intermediate scores between the high and low classes in areas of self-reported antisocial behavior, arrests, and unemployment, suggesting some compromised long-term adjustment.
Declining versus high trajectory class differences
Direct comparisons of the early childhood characteristics of youth who followed a chronic high versus declining trajectory revealed three significant differences. Those who followed a high trajectory pattern were more aggressive and more hyperactive at school (based on teacher report) and more emotionally dysregulated at home (based on parent report) than were children who showed declining symptoms. In addition, although these two groups did not differ significantly on other variables, only the declining class showed elevated social isolation at home and school (relative to the low group), whereas only the high class exhibited elevated emotional distress at school and inconsistent parenting at home (relative to the low group).
These differences are relatively small, and they do not provide definitive information regarding the mechanisms that account for the different developmental pathways experienced by children in the two classes. However, several possibilities exist, which might be explored more fully in future research. First, exposure to stressful life events in early elementary was associated with both high and declining patterns of ADHD, which is consistent with some prior studies that suggest that early family adversity contributes to delays in self-regulatory skill development and thereby may amplify inattentive and hyperactive behavior in early childhood (Bernier et al., 2012; Cicchetti, 2002; Sasser et al., 2014). Theorists have suggested that family adversity might directly increase levels of child emotional distress in ways that distract or overburden regulatory processing and impede executive function maturation in early childhood (Blair & Raver, 2012). In addition, exposure to family life stressors may increase unpredictability and disorganization at home, reducing parental attention, and thereby undermining effective scaffolding of early child self-regulatory development (Sasser et al., 2014).
It is also possible that elevated life stress and biological vulnerabilities contributed to the early ADHD symptoms of children in both the high and declining trajectory classes, but that children in the declining class were more able to benefit from socialization experiences at home and school and thereby showed developmental “catch up” in the later school years. In contrast, children in the high trajectory class, who also experienced inconsistent parenting in addition to elevated life stress, showed more emotional distress at home and more behavioral dysfunction at school, including higher levels of hyperactivity and aggression. The generalization and escalation of hyperactive and aggressive behavior in the school setting may indicate that children with chronic ADHD had greater biological vulnerability and were more impulsive and risk-taking than those in the declining trajectory class; it is also possible that their exposure to inconsistent and ineffective parental discipline in the early years amplified their impulsive and aggressive tendencies (Campbell et al., 2014). These children may have been less amenable to positive socialization efforts at school, and more likely to become enmeshed in coercive interactions with teachers and peers that further undermined self-regulatory skill development, particularly at the transition into adolescence when they gained more autonomy (Beauchaine et al., 2010; Bierman & Sasser, 2014; Cernkovich & Giordano, 2001).
Although social isolation is generally considered a risk factor, it is possible that children in the declining ADHD class, who were more socially withdrawn than children in the low trajectory class, elicited more positive support from teachers and peers than the more socially prominent and disruptive children in the high trajectory class. Considering the poorer late adolescent outcomes of children in the high trajectory, it may be that social isolation also protected children in the declining class from deviant peer influences at the transition into adolescence (Loeber et al., 1993). Future research is needed to explore these or other potential mechanisms associated with declining versus chronic high patterns of ADHD symptoms. Understanding these mechanisms enhances developmental models of the disorder, and may inform areas to target with early intervention.
Limitations
A major strength and unique feature of this study was the availability of repeated parent ratings of ADHD symptoms, which allowed for trajectories that modeled inattention and hyperactivity simultaneously and covered a time period longer than prior studies, from Grade 3 to Grade 12. Additional unique features included data on early child characteristics and family risks that were assessed prior to the trajectories, and a set of important outcomes measured in late adolescence to validate trajectories of clinically significant ADHD symptoms.
At the same time, the study had several limitations. First, the trajectories were based on parent ratings and used dichotomous indicators of clinically significant symptom levels. The availability of repeated parent ratings over time facilitated the modeling of trajectories, but parent ratings are also subject to biases. It is unclear how many of the children rated as having elevated ADHD symptoms in Grade 3 would have been diagnosed with ADHD had a more comprehensive diagnostic evaluation been completed. Parents reported that 22% of the children in the declining trajectory had received “medication to control behavior or attention” by the end of Grade 2 (age 8), whereas 52% of the children in the high trajectory had received medication (compared with just 7% in the low trajectory). This suggests that a relatively greater proportion of the children in the high trajectory received medication evaluations associated with their ADHD symptoms (or other behavior problems). The quality of the medication evaluations was likely variable, but it is possible that more of the children in the high group than in the declining group would have qualified for a full diagnosis of ADHD had more complete assessments been employed in the current study.
In addition, the nature of this sample must be taken into account when interpreting the findings. Children were selected for this sample based on elevated conduct problem behaviors at kindergarten entry. Hence, the study provides rich information regarding the diverging development and outcomes of a subset of children with ADHD symptoms, specifically those with early aggressive and oppositional behaviors. The results may not adequately characterize the development of children with ADHD symptoms who do not show concurrent early conduct problem behavior. In addition, this study focused on risk factors typically associated with conduct problems; future research should also explore temperament and cognitive factors that might also differentiate developmental trajectories. For instance, it is possible that executive function skill development may predict diverging inattention/hyperactivity trajectories.
Third, the design of this study does not make it possible to determine whether or how the amount and kind of treatment experienced by children may have influenced their developmental trajectories. By the end of Grade 2, many of the parents in the sample reported that their children had received some kind of “treatment for emotional or behavioral difficulties” at school or at home (25% of the children in the low group, 49% of the children in the declining group, 80% of the children in the high group). This high rate of service use reflects the high-risk nature of the sample, which was selected for elevated conduct problems. However, the nature and quality of services across these high-risk settings was likely highly variable. The study findings represent developmental trajectories and outcomes that occur given “treatment as usual” in economically disadvantaged communities in four diverse geographical regions of the United States.
Fourth, this study used person-oriented analyses to characterize subgroups within the sample, making it possible to identify classes that showed diverse, nonlinear covariation in clinically significant inattentive and hyperactive symptom patterns over time. While this modeling strategy has many advantages, direct comparisons with other studies are limited by variations in samples, modeling strategies, and measurement that affect the trajectories identified. In this study, inattention and hyperactivity were modeled simultaneously and clinically significant cut-offs were used to better understand developmental variation in disordered levels of ADHD symptoms. In contrast, other studies have modeled symptom severity, which may provide additional information to inform trajectory patterns.
Finally, although this study utilized a strong longitudinal design to examine predictors of discontinuity, it cannot specify causal relationships, because it is possible that other processes beyond those examined in the current analyses contributed to the observed associations.
Clinical Implications and Conclusions
The findings suggest that a developmental perspective may be critical for understanding the clinical course of ADHD. Variable-centered analyses tend to emphasize linear associations across time. In contrast, the person-oriented trajectory model used in this study reveals important nonlinear associations characterizing different developmental profiles of clinically significant ADHD symptoms that may inform clinical assessment and treatment. For example, although hyperactivity symptoms decline over time, the trajectories that emerged in this sample suggest that hyperactivity in childhood may be salient in predicting chronicity, particularly when hyperactivity is observed across the home and school settings, and also when it is accompanied by aggressive behavior. In addition, given their association with differential developmental trajectories in this study, emotional difficulties, including emotion dysregulation and emotional distress, may need more attention in ADHD treatment models that tend to focus primarily on behavioral and cognitive impairments (see Shaw et al., 2014). Recognizing that many children with childhood ADHD improve over time, an important, unanswered question for future research is whether preventive interventions during the early school years designed to target key developmental factors might successfully divert more children with ADHD from the stable high to a declining trajectory class, with corresponding long-term benefits (see Chacko, Wakschlag, Hill, Danis, & Espy, 2009). Future research of this kind is needed to help to fill in the gaps in the existing literature, and illuminate the developmental mechanisms that may underlie diverse developmental trajectories of ADHD symptoms. In turn, a better understanding of the developmental course and processes associated with ADHD trajectories may inform more effective prevention and intervention approaches.
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Submitted: January 15, 2015 Revised: August 11, 2015 Accepted: August 14, 2015
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Source: Journal of Abnormal Psychology. Vol. 125. (2), Feb, 2016 pp. 207-219)
Accession Number: 2016-06080-006
Digital Object Identifier: 10.1037/abn0000112
Record: 16- Title:
- Effects of sexual assault on alcohol use and consequences among young adult sexual minority women.
- Authors:
- Rhew, Isaac C.. Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, US, rhew@uw.edu
Stappenbeck, Cynthia A.. Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, US
Bedard-Gilligan, Michele. Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, US
Hughes, Tonda. College of Nursing, University of Illinois at Chicago, Chicago, IL, US
Kaysen, Debra. Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, US - Address:
- Rhew, Isaac C., Department of Psychiatry and Behavioral Sciences, Center for the Study of Health and Risk Behaviors, University of Washington, 1100 North East 45th Street, 300, Seattle, WA, US, 98105, rhew@uw.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 85(5), May, 2017. pp. 424-433.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 10
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- sexual assault, alcohol use, sexual minority women, marginal structural model
- Abstract (English):
- Objective: The purpose of this study was to examine effects of sexual assault victimization on later typical alcohol use and alcohol-related consequences among young sexual minority women (SMW). Method: Data were collected over 4 annual assessments from a national sample of 1,057 women who identified as lesbian or bisexual and were 18- to 25-years-old at baseline. Marginal structural modeling, an analytic approach that accounts for time-varying confounding through the use of inverse probability weighting, was used to examine effects of sexual assault and its severity (none, moderate, severe) on typical weekly number of drinks consumed and number of alcohol-related consequences 1-year later as well as 2-year cumulative sexual assault severity on alcohol outcomes at 36-month follow-up. Results: Findings showed that compared with not experiencing any sexual assault, severe sexual assault at the prior assessment was associated with a 71% higher number of typical weekly drinks (count ratio [CR] = 1.71; 95% confidence interval [CI] [1.27, 2.31]) and 63% higher number of alcohol-related consequences (CR = 1.63; 95% CI [1.21, 2.20]). Effects were attenuated when comparing moderate to no sexual assault; however, the linear trend across sexual assault categories was statistically significant for both outcomes. There were also effects of cumulative levels of sexual assault severity over 2 years on increased typical drinking and alcohol-related consequences at end of follow-up. Conclusions: Sexual assault may be an important cause of alcohol misuse among SMW. These findings further highlight the need for strategies to reduce the risk of sexual assault among SMW. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Impact Statement:
- What is the public health significance of this article?—Sexual assault during young adulthood may be a cause of alcohol misuse among sexual minority women. Identification and implementation of effective strategies to prevent sexual assault in this population may reduce the disparity in alcohol misuse between sexual minority and heterosexual women. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Alcohol Abuse; *Alcohol Drinking Patterns; *Bisexuality; *Lesbianism; *Sex Offenses; Human Females; Minority Groups
- PsycINFO Classification:
- Psychological Disorders (3210)
- Population:
- Human
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Daily Drinking Questionnaire
Sexual Experiences Survey--Revised
Drinking Norms Rating Form--Modified Version
Sexual Assault Severity Scale
Generalized Anxiety Disorder-7 Scale
Posttraumatic Stress Disorder Checklist
Center for Epidemiologic Studies Depression Scale
Traumatic Life Events Questionnaire DOI: 10.1037/t00545-000
Brief Young Adult Alcohol Consequences Questionnaire DOI: 10.1037/t03955-000 - Grant Sponsorship:
- Sponsor: National Institute on Alcohol Abuse and Alcoholism, US
Grant Number: R01AA018292
Recipients: Kaysen, Debra
Sponsor: National Institute on Alcohol Abuse and Alcoholism, US
Grant Number: K08AA021745
Recipients: Stappenbeck, Cynthia A.
Sponsor: National Institute on Alcohol Abuse and Alcoholism, US
Grant Number: R34AA022966
Recipients: Bedard-Gilligan, Michele - Methodology:
- Empirical Study; Longitudinal Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Mar 13, 2017; Accepted: Jan 28, 2017; Revised: Jan 23, 2017; First Submitted: Aug 22, 2016
- Release Date:
- 20170313
- Correction Date:
- 20170420
- Copyright:
- American Psychological Association. 2017
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/ccp0000202
- PMID:
- 28287804
- Accession Number:
- 2017-11101-001
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2017-11101-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2017-11101-001&site=ehost-live">Effects of sexual assault on alcohol use and consequences among young adult sexual minority women.</A>
- Database:
- PsycINFO
Effects of Sexual Assault on Alcohol Use and Consequences Among Young Adult Sexual Minority Women
By: Isaac C. Rhew
Department of Psychiatry and Behavioral Sciences, University of Washington;
Cynthia A. Stappenbeck
Department of Psychiatry and Behavioral Sciences, University of Washington
Michele Bedard-Gilligan
Department of Psychiatry and Behavioral Sciences, University of Washington
Tonda Hughes
College of Nursing, University of Illinois at Chicago
Debra Kaysen
Department of Psychiatry and Behavioral Sciences, University of Washington
Acknowledgement: This work was supported by funding from the National Institute on Alcohol Abuse and Alcoholism (R01AA018292 awarded to Debra Kaysen, K08AA021745 awarded to Cynthia A. Stappenbeck, and R34AA022966 awarded to Michele Bedard-Gilligan).
Although reasons for the disparity are unclear, numerous studies have shown that sexual minority women (SMW) are more likely than heterosexual women to experience sexual assault in childhood and in adulthood. In a recent systematic review of the literature, prevalence of lifetime sexual assault ranged from 16% to 85% among SMW, with a median estimate of 43% of lesbian and bisexual women reporting adult or childhood sexual victimization across studies (Rothman, Exner, & Baughman, 2011). As noted by Rothman, Exner, and Baughman (2011), most studies on sexual victimization among SMW have focused predominantly on childhood sexual abuse, indicating the need for additional research that examines adult sexual assault in this population.
Despite recent advances in societal acceptance of sexual minorities in the U.S., negative social attitudes and behaviors toward SMW are still widespread (Herek, 1997). The minority stress model posits that the cumulative stressors associated with sexual minority status can lead to negative physical and mental health outcomes and help to explain health disparities (Hatzenbuehler, Phelan, & Link, 2013; Meyer, 2003). There is a growing body of evidence indicating that experiences of interpersonal and institutional discrimination increase problematic drinking among sexual minorities (Hatzenbuehler, Keyes, & Hasin, 2009; McCabe, Bostwick, Hughes, West, & Boyd, 2010; McLaughlin, Hatzenbuehler, & Keyes, 2010). Indeed, young adult SMW are at significantly elevated risk of heavy episodic drinking (consuming four or more drinks in 2 hours; National Institutes on Alcohol Abuse & Alcoholism, 2004) and of experiencing problems related to alcohol use compared with heterosexual women (Drabble, Midanik, & Trocki, 2005; McCabe, Hughes, Bostwick, West, & Boyd, 2009; Wilsnack et al., 2008). For example, lesbian and bisexual women aged 20 to 34 reported higher weekly alcohol consumption and less abstinence compared with older lesbian and bisexual women and to heterosexual women (Gruskin, Hart, Gordon, & Ackerson, 2001). In a study of college females, lesbian/bisexual women were 10.7 times more likely to consume alcohol than heterosexual women (Ridner, Frost, & Lajoie, 2006). Elevated risk of sexual assault has been generally included as an example of structural stigma or minority stress disproportionately affecting sexual minorities (Balsam, Lehavot, & Beadnell, 2011; Hughes, Johnson, & Wilsnack, 2001; Hughes et al., 2010). As such, one contributor to this elevated risk for heavy episodic drinking may be SMW’s greater exposure to sexual assault (Drabble, Trocki, Hughes, Korcha, & Lown, 2013). In both heterosexual women and SMW, those who experience sexual assault may be at greater risk for a variety of health consequences, including alcohol misuse. For example, a history of childhood sexual abuse is associated with later alcohol abuse and dependence (Afifi, Henriksen, Asmundson, & Sareen, 2012; Danielson et al., 2009; Gilmore et al., 2014). Although not entirely consistent, cross-sectional research has found that SMW who report sexual assault in adulthood are also more likely to engage in greater alcohol consumption and heavy episodic drinking than those who report no lifetime sexual assault (Hughes et al., 2010; Ullman, 2003; Wilsnack, Wilsnack, Kristjanson, Vogeltanz-Holm, & Windle, 2004). This may be explained at least in part by negative reinforcement models that suggest that alcohol use may reduce distress, which negatively reinforces continued and increased use of alcohol (Baker et al., 2004).
Longitudinal research is needed to establish temporal ordering and potential causation. Multiple studies in general female samples that did not explicitly consider sexual orientation have observed associations between sexual assault victimization and subsequent alcohol outcomes (e.g., Bryan et al., 2016; Parks, Hsieh, Taggart, & Bradizza, 2014; Testa, Hoffman, & Livingston, 2010; Ullman, 2016). To our knowledge, however, no prospective studies have examined these associations among SMW specifically. As longitudinal research moves forward on this topic, there are important methodologic issues that should be considered. First, the definition of sexual assault varies across studies and ranges from unwanted sexual contact to attempted or completed rape. Given that more severe forms of sexual assault tend to be more strongly associated with negative outcomes (Turchik, 2012), differing levels of assault severity may be associated with differing levels of drinking and alcohol-related problems. The tactics used by the perpetrator to obtain unwanted sex or sexual contact (i.e., verbal coercion, intoxication, and force or threats of force) may play a role in subsequent drinking by the victim and are not always considered (Littleton, Grills-Taquechel, & Axsom, 2009; Zinzow et al., 2012). Further, researchers often ignore the frequency of sexual assault experiences which is problematic given both high occurrence of repeated victimization and associations between the frequency of victimization and negative health consequences (Jozkowski & Sanders, 2012). Thus, a measure of sexual assault severity that incorporates both the severity of the assault experienced as well as the frequency of prior assaults may provide a more sensitive measure of “dose” of exposure and, thus, be a stronger predictor of health consequences.
Second, longitudinal studies with repeated measures of sexual assault exposure and alcohol-related outcomes provide opportunities to examine different aspects of how sexual assault could lead to alcohol misuse. For example, longitudinal studies can examine lagged longitudinal effects of sexual assault on alcohol outcomes at the following year across multiple study assessments. Further, the effects of cumulative exposure to sexual assault across multiple study waves on alcohol use and problems can be examined. SMW appear to be at elevated risk for not only any lifetime sexual assault victimization, but also revictimization (Hughes et al., 2010). Cross-sectional research suggests that individuals experiencing multiple victimization experiences may be at particularly elevated risk for substance use disorders (Hughes, McCabe, Wilsnack, West, & Boyd, 2010).
Finally, obtaining unbiased estimates of effects in longitudinal studies with repeated measures of the exposure can be challenging due to the additional possibility of time-varying confounding. For example, when examining effects of sexual assault on alcohol-related outcomes, it is possible that associations may be confounded by other time-varying factors such as prior levels of mental health symptoms (e.g., depression, anxiety, posttraumatic stress disorder), alcohol use, and prior trauma exposure. Approaches such as marginal structural modeling can be used to account for time-varying confounding and reduce potential bias through the use of inverse probability weights (IPWs) of exposure (Robins, Hernan, & Brumback, 2000). Marginal structural models use a counterfactual framework that can estimate an average causal effect comparing the potential outcome had a given individual been set to one covariate history versus a different covariate history, possibly contrary to the fact (VanderWeele, Hawkley, Thisted, & Cacioppo, 2011). Rather than include time-varying and other confounders in the statistical model, this approach weights subjects by the inverse probability of their own exposure (e.g., sexual assault) status according to covariates. Thus, when applying the IPWs, individuals who are underrepresented for their sexual assault status according to covariate history are given greater weight, whereas those who are overrepresented for a certain exposure level are given lower weight. This results in a “pseudo-population” with balanced distribution of time-varying and time-fixed covariates across levels of the exposure history, and application of IPWs should yield unconfounded estimates of the effects of sexual assault. Further introduction to marginal structural modeling and its applications are found elsewhere (e.g., Bodnar, Davidian, Siega-Riz, & Tsiatis, 2004; Thoemmes & Ong, 2016). To our knowledge, no longitudinal studies in general samples or in SMW-specific samples have utilized marginal structural modeling to investigate the potential causal relation between sexual assault and subsequent alcohol use and consequences.
In the current study we used marginal structural modeling to examine the effects of sexual assault severity assessed at one study wave on typical alcohol use and drinking-related consequences at the next annual study wave in a national sample of young adult SMW. In addition, we also examined whether cumulative exposure to sexual assault over 2 years was associated with alcohol use and consequences at the final study wave.
Method Participants and Procedures
Participants in this study were part of a longitudinal study of young adult (ages 18 to 25) SMW’s health and health behaviors. Women were recruited to participate via advertisements placed on the social networking website Facebook in such a way that only women who reported lesbian or bisexual identity on their profile and who were between the ages of 18–25 were shown the ad. Upon logging into Facebook, potential participants were shown the study advertisement (displayed in the side bar) with a link to the screening assessment. We used two types of advertisements: those that included sexual-minority-specific content (e.g., “LGB women needed for an online study on health behaviors”) and those that were non-LGB-specific (e.g., “We need you for an online study on partying”). Online advertisements were also placed on Craigslist in 12 metropolitan areas in the U.S.: Atlanta, Austin, Boston, Chicago, Houston, Los Angeles, New York, Philadelphia, San Francisco, Seattle, South Florida, and Washington, DC. Craigslist postings included a brief summary of the project and a URL link to the screening assessment.
Women who responded to the advertisements and accessed the study screening site were first shown information about the study. After agreeing to participate, potential participants were then routed to a 5-min screening assessment. A total of 4,119 women completed the online screening survey. Study eligibility criteria included: (a) residing in the U.S.; (b) having a valid e-mail address; (c) being between the ages of 18 and 25 years; and (d) reporting lesbian or bisexual identity at the time of the assessment. Eligible women (n = 1,877) were then invited to participate in the study. Of those eligible, 1,083 (57.7%) provided consent for participation in the larger study. Inconsistencies in the data that suggested a small number of participants were falsifying information (e.g., inconsistent birth dates over time) led us to omit 2.4% of the participants in the baseline sample; 1,057 were retained in the study. Compared with those who were retained in the study, those women who were eligible and did not consent were less likely to be White race (67.9% vs. 78.8%; χ2(1) = 30.5; p < .001) and more likely to be of Hispanic ethnicity (13.4% vs. 10.2%), χ2(1) = 4.6, p = .03. However, there were no statistically significant differences in age or sexual orientation.
Data were collected online at four annual assessments. Participants were paid $25 for completion of the baseline survey and $30 for completion of each of the three annual follow-up assessments. A Federal Certificate of Confidentiality was obtained for the study. The University’s Institutional Review Board reviewed and approved all study procedures.
Measures
Sexual assault
The revised Sexual Experiences Survey (SES) was used to assess sexual assault severity (Koss et al., 2007). This measure asks about experiences of different types of unwanted sexual behaviors (e.g., fondling, attempted or completed oral, vaginal or anal penetration) and tactics used to obtain each outcome (e.g., coercion, intoxication, and threat or use of physical force). Participants indicated how often (0 = never to 3 = 3 or more times) they experienced each unwanted sexual behavior by each tactic (e.g., how often they experienced attempted vaginal sex by coercion). At the baseline assessment, questions were asked in reference to “since age 18” and in follow-up assessments the reference period was “in the past year” (i.e., the time since the last assessment). An overall severity score was calculated as described by Davis and colleagues (2014). First, each sexual experience and tactic combination was assigned a severity rank from 0 (no history of sexual assault) to 6 (attempted or completed rape by threat or use of physical force). For each sexual experience and tactic combination, the severity rank was multiplied by the frequency of its occurrence and then summed for a total combined severity-frequency score with a possible range of 0–63. This severity score has shown strong convergent validity, especially among populations that report higher rates of assault (Davis et al., 2014).
Alcohol consumption
Typical weekly alcohol consumption during the previous 3 months was assessed using the Daily Drinking Questionnaire (DDQ; Collins, Parks, & Marlatt, 1985). Participants were asked “Consider a typical week during the last 3 months. How much alcohol (measured in number of standard drinks), on average, do you drink each day of a typical week?” Standard drinks were defined as 1.5 oz. of liquor, 5 oz. of wine, or 12 oz. of beer. Typical weekly drinking was the sum of the number of standard drinks for each day of the typical week.
Alcohol consequences
Alcohol consequences were measured using the Young Adult Alcohol Consequences Questionnaire (YAACQ; Read, Kahler, Strong, & Colder, 2006). The YAACQ obtains self-ratings of 48 possible drinking consequences. For each of the 48 consequences, participants indicate whether or not they experienced that consequence in the previous 30 days. The sum of the responses was calculated for a count of the number of past month alcohol consequences.
Covariates
A number of demographic and other measures were used for estimation of the predicted probability of past year sexual assault and level of severity. Demographic characteristics included age at baseline, race/ethnicity, sexual identity (lesbian, bisexual), and parent’s highest level of education. Additional psychosocial constructs were included because they tend to co-occur with sexual assault. Mental health problems were assessed using validated and commonly used measures including the Post traumatic stress disorder (PTSD) Checklist (PCL) to assess posttraumatic stress disorder symptoms (Ruggiero, Del Ben, Scotti, & Rabalais, 2003), the Center for Epidemiologic Studies Depression (CES-D) Scale to assess depression symptoms (Radloff, 1977), and the GAD-7 to assess generalized anxiety symptoms (Spitzer, Kroenke, Williams, & Lowe, 2006). The Daily Heterosexist Experiences Questionnaire (DHQ) was used to assess participants’ perceived minority stress due to their LGBT identity (Balsam, Beadnell, & Molina, 2013). Items ask about 38 stressors that LGBT individuals might experience such as difficulty finding a partner, pretending to be heterosexual, and being verbally harassed due to being LGBT. For each of the above psychosocial scales, the internal consistency was >.90 in this sample. Other traumatic events were assessed using the Traumatic Life Events Questionnaire (TLEQ; Kubany et al., 2000). At baseline, this measure asked about events in reference to one’s lifetime, but at annual follow-up visits the reference period was the past year. Further, the baseline TLEQ asked about sexual assaults that were experienced before age 13 and between age 13 and 18. Perceived drinking norms for sexual minority women were also assessed using a modified version of the Drinking Norms Rating Form (Baer, Stacy, & Larimer, 1991; Litt, Lewis, Rhew, Hodge, & Kaysen, 2015) that asked participants about the typical number of drinks consumed per week by a typical lesbian or bisexual woman.
Analytic Plan
We used marginal structural modeling to examine effects of sexual assault severity on alcohol use and drinking-related consequences. As the first step for these analyses, the inverse probability weights were calculated. To derive the IPWs for past year sexual assault, we first calculated the probability for being at one’s own level of sexual assault severity at each of the follow-up time points according to time-varying covariates measured at prior assessments as well as baseline covariates. Time-varying covariates included earlier levels of alcohol use and drinking-related consequences, past year sexual assault severity, other traumatic events, perceived heterosexism, perceived descriptive norms, PTSD symptoms, depression symptoms, and generalized anxiety symptoms. Baseline time-fixed covariates included age, race (White, Black, other race), Hispanic ethnicity, sexual identity (bisexual, lesbian), highest level of parents’ education, and sexual assault prior to age 18 (none, childhood = <13 years, adolescent = 13–18 years).
The distribution of the SES score at each of the follow-up visits was severely positively skewed with the vast majority of scores being 0 (>70%). Because of this, calculation of weights for the continuous SES score based on a probability density using a linear or log-linear model could lead to biased estimates (Naimi, Moodie, Auger, & Kaufman, 2014). The SES score was, thus, recategorized into three groups: (a) no sexual assault (score of 0); (b) moderate severity (score of 1 to 6); and (c) high severity (score of 7 or higher). These categories were selected in order to ensure that there were sufficient numbers within each category and that the size of the two highest bins was similar. Because of the categorical nature of this measure we used a cumulative probability (ordinal logistic) regression model to regress the sexual assault category at each relevant exposure time point (12- and 24-month assessment) on covariates reported prior to this time point. Model-predicted probabilities were used to derive the probability of one’s observed category of exposure (e.g., the predicted probability of being in the highest category of sexual assault severity for a woman who was actually in the highest category of severity) at each time point. These predicted probabilities served as the denominator of the IPWs. To improve precision of estimates, we used stabilized IPWs such that the numerator of the IPWs was the predicted probability of one’s own level of sexual assault severity according to time-fixed covariates only (e.g., race, baseline age). Thus, the stabilized IPW (SW) for subject i at follow-up visit j is defined as
where A is the category of sexual assault victimization, k is the level of exposure,
and
represent the exposure and covariate history up to time j, and V represents a vector of time-fixed covariates. Further, we truncated IPWs such that extreme low or high values were recoded to the first or 99th percentile in order to increase precision of estimates (Cole & Hernan, 2008).
A first set of models examined lagged effects of sexual assault severity at wave j − 1 on alcohol outcomes assessed at the following wave, j. For this set of models, the weights were applied to generalized estimating equations (GEE) models with robust standard errors and a working independence correlation in order to account for nesting of observations within individuals (Diggle, Heagerty, Liang, & Zeger, 2002). The alcohol outcome (typical drinks per week or alcohol-related consequences) at study time point j was regressed on sexual assault severity at the previous time point, j − 1. Because both alcohol outcomes were discrete counts that showed evidence of overdispersion, a negative binomial rather that Poisson form of the model was used. In negative binomial models, covariates are connected to the outcome via a log link. Coefficients for covariates are often exponentiated (eβ) to yield count ratios (CRs; also referred to as rate ratios) that describe the proportional change in the count associated with a one-unit increase in the covariate (Atkins & Gallop, 2007; Hilbe, 2014). Baseline time-fixed covariates were included in the GEE model to improve precision of parameter estimates (Cole & Hernan, 2008). The second set of models examined the effects of cumulative sexual assault severity over 2 years on the alcohol outcomes at the 36-month assessment. Cumulative severity was defined as the sum of SES categories at 12- and 24-month assessments (possible range: 0 to 4). Because only the 36-month outcomes were examined for these models, a single-level (non-GEE) negative binomial regression model was performed. The IPWs for this set of models was the product of the two wave-specific (12- and 24-month) IPWs. The same baseline time-fixed covariates were included in these models. For comparison, we also ran “traditional” nonweighted models that included the same time-fixed covariates for both the lagged and cumulative models.
As post hoc analyses, we also examined whether effects of sexual assault differed by baseline sexual orientation (lesbian vs. bisexual) by including sexual assault by sexual orientation interaction terms into the models.
There was 20%, 28%, and 30% of the sample missing data at 12-month 24-month and 36-month assessments, respectively. To account for missingness, we used multiple imputation where missing values were imputed according to various covariates including earlier and later measures of the variable (Graham, 2009). Assuming data are missing at random (MAR) such that missingness is not associated with unmeasured variables, parameter estimates using multiple imputation should be unbiased. At any given study visit, missingness of typical weekly drinking and alcohol-related consequences were not statistically significantly associated with those same measures from earlier or later assessments. Although not offering definitive proof, this is consistent with the MAR assumption. Imputation was performed using the multiple imputation chained equations (MICE) approach and 20 imputed data sets were created (Azur, Stuart, Frangakis, & Leaf, 2011; White, Royston, & Wood, 2011). Within each imputed dataset, the IPWs were calculated and then the weighted model was run. Parameter estimates were combined across the data sets and standard errors were calculated to account for the uncertainty of imputed values according to Rubin’s rules (Rubin, 2004). All analyses were performed using Stata 14 (StataCorp, College Station, TX).
ResultsTable 1 displays the distribution of selected demographics and other characteristics of the study sample. Nearly 60% of the sample identified as bisexual. The proportion of participants who had clinically elevated scores for depression, GAD, or PTSD was notably elevated compared with general population and primary care samples (Lowe et al., 2008; Mair et al., 2009; Stein, McQuaid, Pedrelli, Lenox, & McCahill, 2000; Walker, Newman, Dobie, Ciechanowski, & Katon, 2002). Consistent with extant literature, history of sexual assault was common with 39% of the sample reporting sexual assault during childhood (before age 13) and 35% reporting sexual assault during adolescence (between ages 13 and 18).
Distribution of Baseline Characteristics
Reports of sexual assault in adulthood were also common. As shown in Table 2, more than one half of the sample reported experiencing moderate or severe sexual assault between their 18th birthday and the baseline survey. Further, at each annual follow-up assessment more than 20% of the sample reported some form of sexual assault in the previous year. Based on the continuous version of the Sexual Assault Severity Scale (range: 0 to 30), the mean score was 2.0 at the 12- (SD = 5.2) and 24-month (SD = 5.4) follow-up visits and 1.6 (SD = 5.0) at the 36-month visit. Table 2 also shows levels of typical weekly drinking and drinking-related consequences at each of the study assessments.
Levels of Alcohol Use and Consequences and Sexual Assault Severity Across Study Assessments
Table 3 presents results from the first set of models that examined the effects of prior year sexual assault severity on typical level of drinking. According to findings from the marginal structural model, past year severe sexual assault was associated with a 71% higher count of typical drinks per week compared to no sexual assault (CR = 1.71; 95% CI [1.27, 2.31]). However, when comparing moderate to no sexual assault, there was no statistically significant association (CR = 1.13; 95% CI [0.85, 1.52]). The linear trend as assessed when modeling the SES as a single ordinal variable was statistically significant (p = .023). Notably, when using a “traditional” unweighted GEE model that adjusted only for the time-fixed covariates, the magnitude of associations was stronger compared to the weighted model. This suggests that effect sizes from the traditional model may be upwardly biased due to time-varying confounders not accounted for in the model.
Count Ratios for Typical Drinks per Week According to Sexual Assault Severity and Other Time-Fixed Covariates from Marginal Structural and Unweighted Models
Sexual assault severity also appeared to have effects on alcohol-related consequences (see Table 4). Similar to findings of typical drinking, past year severe sexual assault compared to no sexual assault was associated with a 63% higher level of alcohol consequences at the subsequent assessment (CR = 1.63; 95% CI [1.21, 2.20]). Although the association was not as strong, moderately severe compared to no sexual assault was also related to higher levels of alcohol-related consequences the following year (CR = 1.42; 95% CI [1.10, 1.84]). Further, the linear trend was statistically significant (p = .004). Again, nonweighted effect estimates were stronger than the weighted estimates.
Count Ratios for Alcohol Related Consequences According to Earlier Sexual Assault Severity and Other Time-Fixed Covariates from Marginal Structural and Unweighted Models
Effects of cumulative sexual assault severity over two years on alcohol outcomes at the 36-month follow-up assessment were also examined (see Table 5). Examining typical drinking as the outcome, findings from the marginal structural model showed that a one-unit increase in 2-year cumulative sexual assault severity categorical score was associated with a 27% increase in count of drinks per week at the final follow-up visit (CR = 1.27; 95% CI [1.14, 1.42]). There was also a strong association between 2-year cumulative sexual assault severity and drinking related consequences (CR = 1.27; 95% CI [1.12, 1.43]). Similar to analyses examining the 1-year lagged effects, findings showed stronger associations for both typical drinking and drinking consequences when using unweighted models. Parameters for other covariates (not shown) were similar to those from corresponding models shown in Tables 4 and 5. For the lagged and cumulative models, there was no evidence of moderation of sexual assault severity effects by sexual orientation for either alcohol outcome.
Count Ratiosa for Typical Drinks per Week and Alcohol-Related Consequences at 36-Month Follow-Up According to 2-Year Cumulative Sexual Assault Severity Based on Reports from 12- and 24-Month Follow-Up
DiscussionResults from marginal structural models indicated that there were deleterious effects of sexual assault on both typical drinking and alcohol-related consequences the following year. These effects were stronger with increasing levels of sexual assault severity. Further, there also appeared to be a cumulative effect of sexual assault on alcohol use where cumulative sexual assault severity over a 2-year period also predicted greater levels of drinking and more alcohol-related consequences. These findings point to the importance of prevention programming for SMW to decrease prevalence of sexual assault victimization and revictimization.
Consistent with other studies of prevalence of sexual assault exposure in SMW across the life span, we found high occurrence of exposure among young SMW (Balsam, Rothblum, & Beauchaine, 2005; D’Augelli, Pilkington, & Hershberger, 2002; Hughes et al., 2001; Hughes et al., 2010). In a review of 75 studies that included SMW, the median estimate of lifetime sexual assault was 43% for SMW (Rothman et al., 2011). In our sample, the prevalence of lifetime sexual assault was even higher—over one half experienced an assault as an adult and roughly one third experienced an assault in childhood or adolescence. This study highlights the important role of adult sexual victimization in understanding potential risk for alcohol use and consequences, even after accounting for multiple putative confounders. Sexual assault, as a particular risk for alcohol use among young SMW, has only relatively recently been an area of focus, while much of the earlier research has focused on child sexual victimization.
The minority stress model, where stress mediates relationships between a stigmatized sexual identity and, in this case, alcohol use and consequences (Meyer, 2003; Talley et al., 2016), has some limitations in that it does not incorporate potential psychological mediators between stress and health outcomes (Hatzenbeuhler, 2009). It also fails to incorporate moderators such as gender. The focus specifically on stressors that occur because of sexual orientation may fail to adequately consider issues of intersectionality, such as victimization that may be multiply determined by gender and by sexual orientation. It is also possible that there are cumulative effects of sexual victimization and discrimination among already marginalized and at risk populations where high intensity stressors such as moderate to severe repeated sexual victimization may have a particularly deleterious effect. More recent theoretical models address potential mechanisms of action for the effects of minority stress on health outcomes including coping and emotion regulation skills, social factors, and maladaptive cognitions (Hatzenbeuhler, 2009). Although this study was not situated to test these potential mechanisms, sexual victimization can lead to higher endorsement of coping motives for drinking, increased tension reduction alcohol expectancies, increased emotion dysregulation, and more negative cognitions about self and others, and higher drinking norms, all of which may help explain increased alcohol use and consequences (Bedard-Gilligan, Kaysen, Desai, & Lee, 2011; Gilmore et al., 2014; Stappenbeck, Bedard-Gilligan, Lee, & Kaysen, 2013; Ullman, Filipas, Townsend, & Starzynski, 2005). There may be an interaction between victimization and discrimination such that effects of these factors are stronger for women who have experienced both. Similarly, the appraisal of the intention of the assault (e.g., related to one’s sexual identity) may also amplify effects of the assault on alcohol outcomes. Future research should examine the explanatory and moderating roles of these factors among SMW.
The study findings have important public health and clinical implications. The widespread occurrence of sexual assault, both at baseline and across the three follow-up assessments, speaks to the vulnerability of this population and to the need for targeted services aimed at risk reduction. Findings highlight the need for targeted strategies to prevent sexual assault among SMW. Further, although sexual assault is a major public health problem in and of itself, implementation of effective sexual assault prevention strategies could also have substantial effects on reducing the burden of alcohol misuse in this population. As has been frequently noted, SMW are an important health disparities population and initiatives such as Healthy People 2020 (U.S. Department of Health & Human Services, 2016) have called for the reduction of sexual-orientation-related disparities across a range of health and behavioral outcomes. Based on the counterfactual framework and marginal structural model results, in this sample the average number of typical drinks per week among women who reported a severe sexual assault in the prior year would have declined from 10.3 to 6.0 and the average number of past-month alcohol-related consequences would have declined from 5.0 to 3.1 had these women actually not experienced any sexual assault. Further, although the difference would be more modest, had they not experienced any sexual assault, women experiencing a moderate sexual assault would have shown a decline from 4.4 to 3.1. In light of the magnitude of these potential reductions in drinking and alcohol-related consequences as well as the elevated prevalence of sexual assault among SMW, the prevention of sexual assault could yield important reductions in the disparity between SMW and heterosexual women in alcohol misuse.
Further, although the blame for victimization lies firmly on the perpetrator, there is clinical utility in implementing effective risk reduction programs to empower potential victims and decrease incidence of victimization among high-risk groups such as SMW. Future studies should seek to explore other factors that may play a role in understanding the relationship between sexual assault and drinking behavior, such as relationship with the perpetrator, the development of posttraumatic stress disorder, and adaptive and maladaptive coping strategies in the aftermath of an assault, to best understand how to improve prevention and intervention efforts for SMW. SMW are at unique increased risk for adverse societal and cultural experiences, such as discrimination and microaggressions, in addition to sexual assault. Thus, better understanding of the ways in which these factors interact and the strategies that can promote recovery and reduce risky behavior, such as drinking, for this population is crucial.
A major strength of this study is its use of marginal structural models to account for time-varying confounding. Using this approach, we were able to isolate and estimate the specific causal pathway from sexual assault to alcohol use adequately accounting for other time-varying factors that can often co-occur with both. The utility of this approach is highlighted when comparing results from the weighted and unweighted models. Results from the unweighted model were consistently stronger than those from the weighted model. This suggests that there are important confounders that were not adequately accounted for in the traditional model. Application of the marginal structural modeling approach in other longitudinal investigations of effects of risk factors on alcohol outcomes could prove beneficial in establishing more accurate effect sizes.
Despite these notable strengths there are limitations that should be considered when evaluating the study outcomes. First, this sample was recruited online and there were differences in racial composition between those who ultimately consented and those who declined participation. It is therefore possible that the sample may not be representative of the general population of young adult SMW in the United States. However, regarding the online sampling, prior studies have shown that online recruitment methods can be reflective of intended populations of interest (Harris, Loxton, Wigginton, & Lucke, 2015). Also, obtaining large samples of hard-to-reach minority populations using traditional sampling methods such as random digit dialing may not be feasible. Finally, as highlighted by recent discussions in epidemiology, although representativeness is necessary for descriptive epidemiologic studies that are intended to report on the health status of a population, it may not be as relevant for studies that are intended to understand causal mechanisms and that have appropriate adjustment for potential confounding (Rothman, Gallacher, & Hatch, 2013). Sexual orientation can change for women over time, yet sexual orientation analytically was treated as a time-fixed covariate. Indeed there was some evidence of sexual orientation change over follow-up. However, the prevalence of change at any follow-up wave was low (<8%). Thus, including this as a time-varying covariate in estimation of IPWs could lead to extreme weights for some participants. Descriptive statistics also suggest that change in orientation showed no significant association with sexual assault at the following wave. Thus, we would not expect changing sexual orientation to bias our findings in any appreciable manner. Another limitation is the use of self-report measures of alcohol use and consequences which may underestimate true levels of consumption and consequences. However, the DDQ and YAACQ, measures used for this study, have been validated against other criterion standards in multiple studies. Further, because of the longitudinal nature of the study, there is a lower likelihood of differential reporting of alcohol use and consequences due to sexual assault history.
To summarize, using a marginal structural modeling approach that accounts for both time-fixed and time-varying confounders, this study found effects of sexual assault on increasing levels of typical drinking and alcohol-related consequences one year later among SMW. Further, 2-year accumulation of sexual assault exposure also appeared to have effects on increasing alcohol use and alcohol-related consequences. This evidence highlights the health consequences of sexual assault and the need to identify effective prevention efforts to reduce risk of sexual assault and its long-term sequelae, especially among SMW. As research into this area progresses, understanding mechanisms through which sexual assault leads to increased drinking is necessary to provide clearer targets of intervention. Further, studies that include both sexual minority and heterosexual women may be informative to compare effects between the two groups. Overall, SMW are a highly vulnerable group for both sexual assault victimization and substance use and clearly there is a need for additional research to understand both shared and unique factors between SMW and heterosexual women that contribute to the increased risk.
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Submitted: August 22, 2016 Revised: January 23, 2017 Accepted: January 28, 2017
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Source: Journal of Consulting and Clinical Psychology. Vol. 85. (5), May, 2017 pp. 424-433)
Accession Number: 2017-11101-001
Digital Object Identifier: 10.1037/ccp0000202
Record: 17- Title:
- Efficacy of mindfulness-based addiction treatment (MBAT) for smoking cessation and lapse recovery: A randomized clinical trial.
- Authors:
- Vidrine, Jennifer Irvin. Stephenson Cancer Center, University of Oklahoma Health Sciences Center, Oklahoma City, OK, US, Jennifer-Vidrine@ouhsc.edu
Spears, Claire Adams. Department of Psychology, Catholic University of America, Washington, DC, US
Heppner, Whitney L.. Department of Psychological Science, Georgia College and State University, Milledgeville, GA, US
Reitzel, Lorraine R.. Department of Educational Psychology, University of Houston, Houston, TX, US
Marcus, Marianne T.. Center for Substance Abuse Prevention, Education and Research, UTHealth School of Nursing, TX, US
Cinciripini, Paul M.. Department of Behavioral Science, University of Texas MD Anderson Cancer Center, Houston, TX, US
Waters, Andrew J.. Department of Medical and Clinical Psychology, Uniformed Services University of the Health Sciences, Bethesda, MD, US
Li, Yisheng. Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, US
Nguyen, Nga Thi To. Department of Health Disparities Research, University of Texas MD Anderson Cancer Center, Houston, TX, US
Cao, Yumei. Michael E. DeBakey Veterans Affairs Medical Center Houston, Houston, TX, US
Tindle, Hilary A.. Division of General Internal Medicine and Public Health, Vanderbilt University, Nashville, TN, US
Fine, Micki. Mindful Living, Houston, TX, US
Safranek, Linda V.. Independent Practice, Kingwood, TX, US
Wetter, David W.. Department of Psychology, Rice University, Houston, TX, US - Address:
- Vidrine, Jennifer Irvin, Stephenson Cancer Center, University of Oklahoma Health Sciences Center, 655 Research Parkway, Suite 400, Office 454, Oklahoma City, OK, US, 73104, Jennifer-Vidrine@ouhsc.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 84(9), Sep, 2016. pp. 824-838.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 15
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- mindfulness, tobacco treatment, group therapy, nicotine dependence
- Abstract (English):
- Objective: To compare the efficacy of Mindfulness-Based Addiction Treatment (MBAT) to a Cognitive Behavioral Treatment (CBT) that matched MBAT on treatment contact time, and a Usual Care (UC) condition that comprised brief individual counseling. Method: Participants (N = 412) were 48.2% African American, 41.5% non-Latino White, 5.4% Latino, and 4.9% other, and 57.6% reported a total annual household income < $30,000. The majority of participants were female (54.9%). Mean cigarettes per day was 19.9 (SD = 10.1). Following the baseline visit, participants were randomized to UC (n = 103), CBT (n = 155), or MBAT (n = 154). All participants were given self-help materials and nicotine patch therapy. CBT and MBAT groups received 8 2-hr in-person group counseling sessions. UC participants received 4 brief individual counseling sessions. Biochemically verified smoking abstinence was assessed 4 and 26 weeks after the quit date. Results: Logistic random effects model analyses over time indicated no overall significant treatment effects (completers only: F(2, 236) = 0.29, p = .749; intent-to-treat: F(2, 401) = 0.9, p = .407). Among participants classified as smoking at the last treatment session, analyses examining the recovery of abstinence revealed a significant overall treatment effect, F(2, 103) = 4.41, p = .015 (MBAT vs. CBT: OR = 4.94, 95% CI: 1.47 to 16.59, p = .010, Effect Size = .88; MBAT vs. UC: OR = 4.18, 95% CI: 1.04 to 16.75, p = .043, Effect Size = .79). Conclusion: Although there were no overall significant effects of treatment on abstinence, MBAT may be more effective than CBT or UC in promoting recovery from lapses. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Impact Statement:
- What is the public health significance of this article?—Although there were no significant differences in overall abstinence between Mindfulness-Based Addiction Treatment (MBAT) and traditional Guideline-based treatments within a diverse and relatively low SES sample of smokers, MBAT may be more efficacious than CBT or UC in facilitating lapse recovery. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Cognitive Behavior Therapy; *Group Counseling; *Nicotine; *Smoking Cessation; *Mindfulness; Drug Dependency; Recovery (Disorders); Relapse Prevention; Treatment Effectiveness Evaluation
- PsycINFO Classification:
- Health & Mental Health Treatment & Prevention (3300)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Mindfulness Technique Practice During Treatment Measure
Smoking Abstinence Measure
Mindfulness Attention Awareness Scale
Kentucky Inventory of Mindfulness
Heaviness of Smoking Index DOI: 10.1037/t04726-000 - Grant Sponsorship:
- Sponsor: National Institute on Drug Abuse, US
Recipients: No recipient indicated
Sponsor: Centers for Disease Control and Prevention, US
Recipients: No recipient indicated
Sponsor: National Cancer Institute, US
Recipients: No recipient indicated
Sponsor: National Center for Complementary and Integrative Health
Recipients: No recipient indicated
Sponsor: National Institute of General Medical Sciences, US
Recipients: No recipient indicated
Sponsor: Oklahoma Tobacco Settlement Endowment Trust, US
Recipients: No recipient indicated - Methodology:
- Clinical Trial; Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: May 23, 2016; Accepted: Apr 8, 2016; Revised: Mar 8, 2016; First Submitted: Dec 31, 2014
- Release Date:
- 20160523
- Correction Date:
- 20160815
- Copyright:
- American Psychological Association. 2016
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/ccp0000117
- PMID:
- 27213492
- Accession Number:
- 2016-25468-001
- Number of Citations in Source:
- 85
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2016-25468-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2016-25468-001&site=ehost-live">Efficacy of mindfulness-based addiction treatment (MBAT) for smoking cessation and lapse recovery: A randomized clinical trial.</A>
- Database:
- PsycINFO
Efficacy of Mindfulness-Based Addiction Treatment (MBAT) for Smoking Cessation and Lapse Recovery: A Randomized Clinical Trial
By: Jennifer Irvin Vidrine
Stephenson Cancer Center and Department of Family and Preventive Medicine, University of Oklahoma Health Sciences Center;
Claire Adams Spears
Department of Psychology, Catholic University of America
Whitney L. Heppner
Department of Psychological Science, Georgia College and State University
Lorraine R. Reitzel
Department of Educational Psychology, University of Houston
Marianne T. Marcus
Center for Substance Abuse Prevention, Education and Research, UTHealth School of Nursing
Paul M. Cinciripini
Department of Behavioral Science, University of Texas MD Anderson Cancer Center
Andrew J. Waters
Department of Medical and Clinical Psychology, Uniformed Services University of the Health Sciences
Yisheng Li
Department of Biostatistics, University of Texas MD Anderson Cancer Center
Nga Thi To Nguyen
Department of Health Disparities Research, University of Texas MD Anderson Cancer Center
Yumei Cao
Michael E. DeBakey Veterans Affairs Medical Center Houston, Texas
Hilary A. Tindle
Division of General Internal Medicine and Public Health, Vanderbilt University
Micki Fine
Mindful Living, Houston, Texas
Linda V. Safranek
Independent Practice, Kingwood, Texas
David W. Wetter
Department of Psychology, Rice University
Acknowledgement: This research and preparation of this article were supported by grants from the National Institute on Drug Abuse, the Centers for Disease Control and Prevention, the National Cancer Institute, the National Center for Complementary and Integrative Health, The National Institute of General Medical Sciences, and the Oklahoma Tobacco Settlement Endowment Trust. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.
The prevalence of smoking in the United States, although declining, remains high at 17.8% (Jamal et al., 2014). Most smokers want to quit, and nearly half of all smokers attempt to quit each year (CDC, 2009), but only about 5% of all smokers successfully quit each year (Cohen et al., 1989). These low quit rates are not surprising given that cessation is associated with increased levels of negative affect and stress that can persist for months, as assessed by both self-report and asymmetries in brain activity (Gilbert et al., 2002; Piasecki, Fiore, & Baker, 1998). This phenomenon is further complicated by a plethora of evidence indicating that stress, negative affect, and depression strongly predict and are setting events for relapse (Baker, Brandon, & Chassin, 2004; Borrelli, Bock, King, Pinto, & Marcus, 1996; Brandon, 1994; Correa-Fernandez et al., 2012; Glassman et al., 1990; Niaura et al., 1999; Shiffman, 2005; Welsch et al., 1999). Thus, an important goal for intervention development research is to carefully target these aversive emotional consequences of quitting smoking in an effort to enhance cessation rates and ultimately prevent relapse. One factor found to be broadly and consistently linked with enhanced emotional regulation is mindfulness, and mindfulness-based treatments may be particularly well suited for treating nicotine dependence and other substance use disorders.
Definition of MindfulnessMindfulness has been defined as “paying attention in a particular way: on purpose, in the present moment, and nonjudgmentally” (Kabat-Zinn, 1994) and as “bringing one’s complete attention to the present experience on a moment-to-moment basis” (Marlatt & Kristeller, 1999). All approaches that include mindfulness note that it should be practiced nonjudgmentally, meaning that to the extent possible, phenomena entering awareness should not be labeled as true or false, good or bad, and so forth (Kabat-Zinn, 1994; Linehan, 1994; Segal, Teasdale, Williams, & Gemar, 2002). A key characteristic of mindfulness is that by simply noticing emotions, cognitions, perceptions, and sensations in a nonjudgmental manner, individuals learn over time that these phenomena are transient and do not demand impulsive action (Heppner, Spears, Vidrine, & Wetter, 2015). Thus, flexible, adaptive responding is fostered when awareness is brought to the present moment (Roemer & Orsillo, 2003; Teasdale, 1997).
Evidence Broadly Supporting Mindfulness-Based TreatmentsThe two most prominent explicitly mindfulness-based treatments are Mindfulness-Based Stress Reduction (MBSR; Kabat-Zinn, 1990) and Mindfulness-Based Cognitive Therapy (MBCT; Segal, Vincent, & Levitt, 2002). MBSR was initially targeted at stress and pain-related disorders, and MBCT was developed to treat chronic or recurrent depressive disorders. Both of these approaches use meditation as the principal means of teaching mindfulness. Numerous meta-analyses have concluded that mindfulness-based treatments are effective across a wide range of conditions and disorders (e.g., stress, pain, anxiety-related disorders, eating disorders, depressive relapse, psychological and physiological outcomes for individuals with vascular disease, multiple sclerosis, fibromyalgia, somatization, and mental health disorders; Abbott et al., 2014; Aucoin, Lalonde-Parsi, & Cooley, 2014; Baer, 2003; Godfrey, Gallo, & Afari, 2015; Hatchard, Lepage, Hutton, Skidmore, & Poulin, 2014; Kabat-Zinn, 1982; Kabat-Zinn, Lipworth, & Burney, 1985; Kabat-Zinn, Lipworth, Burney, & Sellers, 1986; Khoury, Lecomte, Fortin et al., 2013; Khoury, Lecomte, Gaudiano, & Paquin, 2013; Kim et al., 2013; Kristeller & Hallett, 1999; Lakhan & Schofield, 2013; Lauche, Cramer, Dobos, Langhorst, & Schmidt, 2013; Speca, Carlson, Goodey, & Angen, 2000). In addition, multiple reviews of the meditation literature have concluded that MBSR and meditation were effective not only across numerous disorders and populations, but that in many cases, MBSR was effective when the individual treatment groups themselves were heterogeneous with respect to the condition/disorder being treated (Baer, 2003; Chiesa & Serretti, 2009, 2011, 2014; Hofmann, Sawyer, Witt, & Oh, 2010). Importantly, mindfulness-based treatments lead to improvements in both anxious and depressive mood states (Goyal et al., 2014), and mindfulness/metacognitive awareness appears to a key mechanism of action. Furthermore, MBCT has demonstrated strong efficacy in preventing relapse to depression compared to alternative approaches (Brown & Ryan, 2003; Teasdale et al., 2002).
There is a rapidly growing body of published studies that have evaluated the efficacy of mindfulness-based treatments for nicotine dependence and other substance use disorders. Outcomes evaluated have included tobacco and other substance use (Bowen et al., 2009; Bowen & Marlatt, 2009; Brewer et al., 2011; Brewer et al., 2009; Davis, Fleming, Bonus, & Baker, 2007; Davis, Goldberg, et al., 2014), psychological distress, craving, mindfulness (Davis et al., 2007; Davis, Manley, Goldberg, Smith, & Jorenby, 2014), and treatment dropout (Marcus et al., 2007). Although these studies have generally been small with varied outcomes, the results have been promising.
To date, at least six studies have evaluated the efficacy of mindfulness-based treatments for nicotine dependence. Four of the studies (Bowen & Marlatt, 2009; Brewer et al., 2011; Davis, Manley et al., 2014; Davis et al., 2013) found that the mindfulness-based intervention evaluated produced significantly higher smoking abstinence rates than the control treatment. The study conducted by Brewer and colleagues (2011) compared a Mindfulness Training (MT) intervention for smoking cessation to the American Lung Association’s Freedom From Smoking (FFS) treatment (N = 88). Both interventions were delivered in a group format over a 4-week period, twice per week. Smoking abstinence was assessed at the end of treatment and 13 weeks following the end of treatment. MT participants had slightly (although not significantly) higher abstinence rates at the end of treatment (i.e., 36% vs. 15%, p = .06), and significantly higher abstinence rates 13 weeks following the end of treatment (i.e., 31% vs. 6%, p = .01). One of these studies found that although mindfulness-based treatment was not associated with significantly higher abstinence rates compared to standard treatment (25.0% vs. 17.9%), mindfulness-based treatment participants reported significantly greater decreases in smoking urges, perceived stress, and experiential avoidance, and significantly greater increases in mindfulness (Davis, Manley et al., 2014). The remaining study was very small (n = 18) and uncontrolled, but found that an 8-week group mindfulness-based intervention yielded a 7-day biochemically confirmed point prevalence abstinence rate of 56% at 6 weeks following the quit date (Davis et al., 2007). Results further indicated that compliance with mindfulness meditation was positively associated with decreases in stress and affective distress. In addition, compliance with mindfulness meditation was also positively associated with smoking abstinence.
At least three studies have evaluated the efficacy of mindfulness-based treatments for other substance use. Two of these studies found that the mindfulness-based treatments evaluated were associated with significantly lower rates of substance use (Bowen et al., 2009, 2014), whereas the other study found no differences in substance use outcomes between a mindfulness-based treatment and a standard CBT-based control condition (Brewer et al., 2009). In addition, the Bowen and colleagues (2009) study described above found that mindfulness-based treatment was associated with significantly greater increases in acceptance and acting with awareness, and significantly greater decreases in craving.
Mindfulness-Based Addiction Treatment (MBAT)Given that many smokers have a history of failed quit attempts, high levels of nicotine dependence, and/or other comorbidities (Irvin & Brandon, 2000), there is a critical need for new behavioral treatments. Mindfulness-based treatments may add an innovative and important intervention option to the clinical end of the treatment continuum for nicotine dependence. Mindfulness-based treatments may be particularly appropriate given the efficacy of mindfulness-based interventions in reducing emotional distress across exceedingly diverse conditions and populations, and evidence that greater trait mindfulness is associated with higher smoking cessation rates, greater ability to recover from a smoking lapse, and a plethora of beneficial factors (Heppner et al., 2015, 2016; Vidrine et al., 2009; Waters et al., 2009). Furthermore, mindfulness researchers have noted that future rigorous tests of mindfulness-based interventions should include adequate control groups and sufficient power (Baer, 2003; Dimidjian & Linehan, 2003; Roemer & Orsillo, 2003; Teasdale, Segal, & Williams, 1995).
The current study was specifically designed to be responsive to these issues and to build upon the foundation of the studies described above, as well as other previous studies. To the best of our knowledge, the current study is the largest randomized clinical trial to evaluate a mindfulness-based treatment for nicotine dependence or other substance use disorder. This trial was adequately powered to support a rigorous evaluation of the efficacy of Mindfulness-Based Addiction Treatment (MBAT) compared to two control conditions, a Cognitive Behavioral Treatment (CBT) condition that matched MBAT on treatment contact time (i.e., number and length of counseling sessions) and a Usual Care (UC) condition comprised of brief individual counseling sessions based on the Treating Tobacco Use and Dependence Clinical Practice Guideline (Fiore et al., 2008). Although CBT and MBAT were matched on treatment contact time, MBAT included homework assignments whereas CBT did not include such assignments. Given our prior research suggesting that trait mindfulness is associated with significantly higher rates of abstinence and recovery of abstinence following a lapse (Heppner et al., 2016), we hypothesized that individuals randomized to MBAT (vs. CBT or UC) would be more likely to achieve abstinence and to recover abstinence following a smoking lapse.
Method Participants
Participants were recruited from the Houston metropolitan area via local print media. Inclusion criteria included: ≥18 years of age, current smoker with an average of at least 5 cigarettes per day for the past year, motivated to quit smoking within the next 30 days, had a viable home address and phone number, able to read and write in English, an expired air CO level of ≥8 ppm, and provided collateral contact information. Exclusion criteria included: contraindication for nicotine patch use, regular use of tobacco products other than cigarettes, use of bupropion or nicotine replacement products other than the study patches, pregnancy or lactation, another household member enrolled in the study, active substance dependence, current psychiatric disorder or use of psychotropic medications, and participation in a smoking cessation treatment program in the previous 90 days. Study advertisements asked if individuals wanted help with quitting smoking, indicated that counseling and nicotine patches would be provided, and stated that participants would be compensated for their time. All data were collected between January 2007 and February 2010. The study was approved by the institutional review board of The University of Texas MD Anderson Cancer Center and informed consent was obtained from all participants.
Procedures
Following the baseline visit, participants were randomized into UC (n = 103), CBT (n = 155), or MBAT (n = 154) using a form of adaptive randomization called minimization, see Table 3 for an overview of the treatment content and timeline for each of the three conditions. Randomization was based on age, education, race/ethnicity, depression history, and cigarettes per day. Participants and research personnel were not blinded to treatment condition following randomization. Fewer participants were randomized to UC (vs. MBAT and CBT) because we expected that there would be a larger difference in abstinence between MBAT and UC than between MBAT and CBT, and a power analysis revealed that a smaller sample size in the UC group yielded sufficient power. Participant flow through the study is detailed in Figure 1. All groups were given self-help materials and nicotine patch therapy. Patch therapy for participants who smoked >10 cigarettes per day consisted of 4 weeks of 21 mg patches, 1 week of 14 mg patches, and 1 week of 7 mg patches. Patch therapy for participants who smoked 5 to10 cigarettes per day consisted of 4 weeks of 14 mg patches and 2 weeks of 7 mg patches. Self-help materials consisted of the consumer products developed for the 2008 update of the Treating Tobacco Use and Dependence Clinical Practice Guideline (Fiore et al., 2008). The CBT and MBAT groups received eight 2-hr in-person group counseling sessions. UC participants received four 5- to 10-min Guideline-based individual counseling sessions (Fiore et al., 2008).
Treatment Content and Timeline
Figure 1. CONSORT flowchart for recruitment, enrollment, and follow-up assessments.
MBAT Intervention
MBCT represents an adaptation of MBSR that includes techniques from cognitive–behavioral therapy. Because MBAT integrates MBSR with a cognitive–behavioral/relapse prevention theory based approach to smoking cessation, MBAT is closely modeled on MBCT. The rationale and session-by-session instructions for MBCT have been published by Segal and colleagues (2002; Segal, Williams, & Teasdale, 2002). MBAT closely follows the MBCT treatment procedures, but replaces the depression-related material with nicotine dependence-related material. MBAT utilizes the same structure, within and across sessions, as does MBCT.
The core aims of MBAT are derived from MBCT. Those aims are to help individuals: (a) become more aware of thoughts, feelings, and sensations from moment to moment, (b) develop a different way of relating to thoughts, feelings, and sensations, and (c) increase the ability to disengage attention and choose skillful responses to any thoughts, feelings, or situations that arise. Therefore, Sessions 1–4 of MBAT concentrated on learning how to direct and focus attention. Participants were taught to become aware of how little attention is usually paid to what they are doing in their daily life (i.e., how much of their daily lives are spent on “automatic pilot”). In addition, they were taught to become aware of how rapidly the mind shifts between topics. Next, they learned how to not only notice that the mind is wandering, but to bring it back to a single focus on the breath. Furthermore, participants learned how a wandering mind can increase negative thoughts and feelings. For example, fantasies about smoking can lead to feelings of anger about being deprived of cigarettes. Engaging in these thoughts can easily escalate craving such that it becomes more difficult to enact a purposeful, adaptive response. By bringing attention back to the present moment, however, one can disengage from this cascade of thoughts and deal with the situation much more flexibly. For example, one could note that the craving is a sensation or mental event (as opposed to an imperative) and simply notice the sensations nonjudgmentally until they pass, choose to engage in a coping behavior, or bring one’s attention back to the breath, which is designed to refocus attention on the present moment.
It is important to note that MBAT is also similar to MBRP in many respects (Bowen et al., 2009; e.g., teaching mindfulness-based strategies for coping with cravings), but differs in key ways. First, MBRP has been evaluated primarily as an aftercare program, whereas MBAT is intended to serve as a primary treatment approach. Second, MBRP opens with at least 20 min of meditation, whereas MBAT incorporates meditation practice later within each treatment session. We created a manual of the MBAT program for use in this trial (Wetter et al., 2007), with content that paralleled that of CBT (described below) in addition to the mindfulness-based techniques.
The scheduled quit day was on Session 5 for participants randomized to MBAT. Sessions 5–8 focused on continuing to develop awareness of the present moment, along with an expansion of techniques for dealing with problematic thoughts, feelings, and situations. To provide an example, one technique is a “breathing space.” A breathing space involves three steps: (a) bringing attention into the present moment and becoming aware of one’s current experience (thoughts, feelings, and bodily sensations), (b) gathering one’s full attention so that it can be redirected to breathing and using the breath as a tool to anchor oneself in the present moment, (c) followed by expanding the field of awareness around breathing to the entire body. The breathing space occupies a central role in both MBCT and MBAT, can be used in virtually any situation, and is a technique for stepping out of automatic pilot by bringing attention to the present moment. Importantly, the breathing space is a method of generalizing the practice of mindfulness that is developed with formal meditation practices to one’s daily life. Participants were taught that they can utilize a breathing space whenever they become aware of urges, stressful situations, or other problematic phenomena.
Cognitive Behavioral Treatment (CBT)
CBT utilized a fairly standard problem-solving/coping skills training approach based on relapse prevention theory (Marlatt & Gordon, 1985) and the Guideline (Fiore et al., 2008). The treatment is manualized and the manual provides a detailed overview of each session including time estimates for each activity and notes to the therapist highlighting potential participant issues and possible responses/probes. All activities are geared toward promoting smoking cessation and the maintenance of abstinence. Each session has specific objectives, and each activity coincides with a minimum of at least one objective. Salient issues covered include nicotine replacement therapy, commitment to abstinence, social/peer pressure, health issues, motivation to change, commitment to change, and coping with stress. Major topics covered in the eight group sessions included: (a) planning to quit smoking; smoking patterns; tools to quit; (b) nicotine addiction; using the nicotine patch; health impact of smoking; triggers; (c) adjusting the stop smoking plan; (d) stress management tools; (e) nutrition and exercise; (f) coping skills; (g) social factors influencing smoking; costs/benefits of quitting; and (h) tapering off the patch; maintaining abstinence; and review of skills from the program. The scheduled quit day was on Session 5 for participants randomized to CBT in order to match MBAT.
We chose CBT as our control condition because it is an empirically supported and recommended treatment for smoking cessation (Fiore et al., 2000; Fiore et al., 2008). Treatment contact time and assessments were identical in CBT and MBAT, and therapists were completely crossed with treatment group (i.e., CBT and MBAT groups differed only with respect to counseling content). This study design was intended to allow us to carefully delineate MBAT mechanisms and effects from the effects of an empirically supported cessation treatment that was matched on treatment delivery modality (i.e., group), clinical contact (i.e., number of sessions and duration of each session), and therapists.
Usual Care (UC) Intervention
UC participants received four 5- to 10-min individual counseling sessions based on the Guideline (Fiore et al., 2008). UC was intended to be equivalent to the intervention a smoker might receive when asking a health care provider for help. The content of the sessions emphasized problem-solving and coping skills training. The scheduled quit day was on Session 3 for participants randomized to UC.
Treatment Delivery and Integrity
The CBT and MBAT groups were led by two masters-level therapists, both of whom were skilled in delivering MBSR and one of whom held a certification in MBSR awarded by the University of Massachusetts Medical Center. Both therapists had extensive personal mindfulness practices and completed approximately 15 hours of training on the components of treatment related to smoking cessation. All groups were led individually by a single therapist. To ensure that any potential treatment group differences would not be attributable to therapist effects, UC was also delivered by the same two therapists that delivered MBAT and CBT. Therapists were completely crossed with treatments such that each counselor delivered equal numbers of MBAT and CBT groups. Therefore, therapist effects were not controlled for in the analyses.
Overall, 37.7% of participants in MBAT completed all eight group counseling sessions, 53.2% completed between four and seven sessions, and 9.1% completed between one and three sessions. In CBT, 34.8% of participants completed all eight group counseling sessions, 52.9% completed between four and seven sessions, and 12.3% completed between one and three sessions. Of those in UC, 53.4% completed all four in-person individual counseling sessions, 30.1% completed three of the sessions, and 16.5% completed one or two sessions.
Measures
All questionnaires were administered and completed via computer. The measures and variables examined are described below.
Demographics
Demographic variables collected at baseline included age, gender, race/ethnicity, partner status, total annual household income, and educational level.
Nicotine dependence
Nicotine dependence was assessed at baseline using the Heaviness of Smoking Index (HSI) (Kozlowski, Porter, Orleans, Pope, & Heatherton, 1994). The HSI comprises the two items from the Fagerstrom Test for Nicotine Dependence (FTND) that most strongly predict smoking relapse, cigarettes per day (CPD) and minutes to the first cigarette after waking. Given that the HSI comprises only two items, and has demonstrated psychometric equivalence to the FTND in multiple studies (Borland, Yong, O’Connor, Hyland, & Thompson, 2010; Chabrol, Niezborala, Chastan, & de Leon, 2005; Hymowitz et al., 1997; Kozlowski et al., 1994; Schnoll, Goren, Annunziata, & Suaya, 2013), it was chosen to reduce participant burden.
Mindfulness technique practice during treatment
Among participants randomized to the MBAT condition, their practice of mindfulness techniques was assessed weekly during the course of treatment. Participants were asked to report (a) the average number of days spent engaging in any of the mindfulness techniques learned during the MBAT treatment during the previous week, and (b) the average number of days spent during the previous week engaging in specific mindfulness techniques taught during treatment (i.e., sitting meditation, body scan, walking meditation, yoga, and awareness of the breath). This measure was administered at seven time points (i.e., Weeks 2, 3, 4, 5, 6, 7, and 8), and self-reported days spent practicing were averaged across the weeks to generate composite variables that reflected average days per week spent practicing mindfulness techniques in general as well as for each of the specific mindfulness techniques.
Smoking abstinence
Seven-day point prevalence abstinence from smoking was assessed at two time points, 4 weeks following the quit day and 26 weeks following the quit day. Because participants were expected to be using the nicotine patch at the assessment that occurred 4 weeks following the quit day, self-reported abstinence was biochemically confirmed using a CO level <6 ppm. We elected to use a CO cutoff of <6 ppm rather than a cutoff of <10 ppm because some research has indicated that a cutoff of <10 ppm may be too high, and may ultimately result in the misclassification of a proportion of smokers as nonsmokers (Javors, Hatch, & Lamb, 2005). It is important to note that we analyzed our data using both cutoff points, and no statistically significant differences in outcomes were observed. Specifically, at the 4-week assessment, point-prevalence abstinence was defined as self-report of complete abstinence from smoking for the previous 7 days and an expired CO level <6 ppm. Continuous abstinence was defined as self-report of complete abstinence from smoking since the quit day, and an expired CO level <6 ppm.
At the 26-week assessment, point-prevalence abstinence was defined as self-report of complete abstinence from smoking for the previous 7 days and CO level <6 ppm. Continuous abstinence was defined as self-report of complete abstinence from smoking since the quit day, and a CO level <6 ppm. However, those participants who did not attend the in-person 26-week assessment visit were asked to provide a saliva cotinine sample via mail. For those participants who provided a saliva sample (n = 29), a saliva cotinine level cutoff of <20 ng/ml was utilized to biochemically confirm self-reported abstinence from smoking. Of the participants at the 26-week assessment who self-reported abstinence and lacked CO data, none reported use of any nicotine replacement products. Therefore, saliva cotinine levels should not have been affected by therapeutic nicotine use. This collection method has been validated in prior research (McBride et al., 1999).
Lapse recovery
Recovery from a lapse was assessed by examining biochemically confirmed (CO <6ppm), 7-day point prevalence abstinence rates at 26 weeks post quit day among participants who were classified as smoking at the end of treatment.
Data Analysis
Chi-square tests and one-way ANOVA tests were used to evaluate differences between MBAT, CBT, and UC at baseline on demographics, smoking rate, and levels of trait mindfulness. Biochemically confirmed 7-day point prevalence and continuous abstinence assessments were conducted 4 weeks and 26 weeks post quit day. Logistic random effects modeling examined 7-day point prevalence abstinence over time (i.e., at 4 and 26 weeks post quit day) and continuous ratio logit models examined continuous abstinence over time. Both unadjusted and adjusted analyses (controlling for age, education, gender, race/ethnicity, partner status, and HSI scores) were conducted. Because there were no differences between unadjusted and adjusted analyses, only adjusted analyses are reported. In addition, the time indicator (Week 4 and Week 26) was included as a covariate in the logistic random effects and continuous ratio logit models. Consistent with standard practice in smoking cessation trials, completers-only and intent-to-treat analyses (whereby participants lost to follow-up were coded as relapsed) were conducted. In addition, because single imputation methods may be more biased than other approaches to missing data, attrition analyses were conducted to ascertain whether there were any systematic differences between those with complete data versus those lost to follow-up. To further investigate this question, a sensitivity analysis was conducted to examine the effects of varying missing data assumptions.
Because behaviors within groups are often dependent (i.e., influenced by other members of the group), failure to take group effects into account in statistical analyses may lead to inaccurate inferences, particularly in the form of Type I errors (Herzog et al., 2002; Kapson, McDonald, & Haaga, 2012). Therefore, all models examined controlled for group effects. Group effects were controlled for by including a random intercept of treatment group membership to the model to account for the nested structure of the data (i.e., participants being nested in groups). The ICC was 0.005 for the group effect model. Due to the considerably reduced sample size of the lapse recovery group, this estimate of the covariance parameter was numerically unstable. Therefore, we did not calculate the ICC for this model.
Treatment effects of MBAT (vs. CBT and UC) in helping individuals to recover from lapses were also examined. Specifically, logistic random effect modeling was used to examine group differences in 7-day point prevalence abstinence rates over time among participants who were classified as smoking at the end of treatment (adjusting for age, education, gender, race/ethnicity, partner status, and HSI scores). Finally, associations of mindfulness practice with smoking cessation outcomes were examined among individuals in MBAT.
Results Participant Characteristics
Participants (N = 412) were racially/ethnically diverse (48.2% African American, 41.5% non-Latino White, 5.4% Latino, and 4.9% other) and most reported a total annual household income of less than $30,000 (57.6%). The majority of participants were female (54.9%) and were not married or living with a significant other (70.0%). Approximately one third of participants had less than or equal to a high school education or GED. Average smoking rate was 19.9 (SD = 10.1) cigarettes per day, and 38.6% of participants reported smoking their first cigarette within 5 min of waking (see Table 1).
Participant Baseline Demographics, Dependence and Mindfulness by Treatment Group
Baseline Differences
No significant baseline differences in demographics, nicotine dependence, or levels of trait mindfulness emerged among the three groups (see Table 1).
Overall Treatment Effects on Cessation Outcomes
Biochemically verified 7-day point prevalence abstinence rates based on a completers-only approach were 32.5% in UC, 39.1% in CBT, and 42.1% in MBAT at 4 weeks post quit day (1 week following the end of treatment) and 19.1% in UC, 23.8% in CBT, and 19.4% in MBAT 26 weeks post quit day. Using an intent-to-treat approach, 7-day point prevalence abstinence rates were 24.3% in UC, 32.3% in CBT, and 34.4% in MBAT 4 weeks post quit day and 11.7% UC, 15.5% in CBT, and 13.0% in MBAT 26 weeks post quit day (see Figure 2).
Figure 2. Seven-day point prevalence abstinence rates by treatment group at 4 and 26 weeks post quit day (intent to treat). N = 412. See the online article for the color version of this figure.
Logistic random effects model analyses that compared the efficacy of UC, CBT, and MBAT over time yielded no overall significant treatment effects, (F(2, 236) = 0.29, p = .749; intent-to-treat: F(2, 401) = 0.9, p = .407). Consistent with the point prevalence analyses, the main effect of treatment on continuous abstinence was not significant over time using an intent-to-treat or a completers-only approach.
MBAT versus CBT
To examine differences in 7-day point prevalence abstinence between MBAT and CBT, we conducted a separate set of analyses that included only these two treatment groups. A logistic random effects model indicated that there were no significant differences between these two conditions over time (completers only: OR = 1.09, 95% CI: .64 to 1.86, p = .750, Effect Size = .05; intent-to-treat: OR = 1.09, 95% CI: .64 to 1.85, p = .755, Effect Size = .05). Consistent with the point prevalence analyses, the main effect of treatment on continuous abstinence over time was not significant.
MBAT versus UC
To examine differences in 7-day point prevalence abstinence between MBAT and UC, we conducted a separate set of analyses that included only these two treatment groups. Over time analyses indicated that the difference between MBAT and UC was not significant (completers only: OR = 1.32, 95% CI: .67 to 2.60, p = .427, Effect Size = .15; intent-to-treat: OR = 1.58, 95% CI: .84 to 2.99, p = .159, Effect Size = .25). Consistent with the point prevalence analyses, the main effect of treatment on continuous abstinence was not significant over time.
Effects of MBAT in Facilitating Recovery From a Lapse
Among participants classified as smoking on the last treatment session (completers only; n = 145), 14.7% in UC,7.0% in CBT, and 27.8% in MBAT had recovered abstinence 1 week following the end of treatment. Twenty-six weeks following the quit day, 0% in UC, 5.0% in CBT and 10.3% in MBAT had recovered abstinence (completers only; n = 110). Logistic random effects model analyses that examined the effect of treatment on 7-day point prevalence abstinence over time revealed a significant overall treatment effect, F(2, 103) = 4.41, p = .015. Post hoc tests revealed significant differences between MBAT and CBT (MBAT vs. CBT: OR = 4.94, 95% CI: 1.47 to 16.59, p = .010, Effect Size = .88) and between MBAT and UC (MBAT vs. UC: OR = 4.18, 95% CI: 1.04 to 16.75, p = .043, Effect Size = .79).
Intent-to-treat analyses of recovery from a lapse revealed a similar pattern of results (n = 151). Among participants classified as smoking at the last treatment session, 13.2% in UC, 7.0% in CBT, and 26.8% in MBAT regained abstinence 1 week following the end of treatment, and 0% in UC, 3.5% in CBT, and 7.1% in MBAT regained abstinence by 26 weeks post quit day. Logistic random effect modeling analyses examining 7-day point prevalence abstinence over time indicated a significant overall treatment effect (F(2,146) = 4.57, p = .012) among participants classified as smoking at the last treatment session. Post hoc tests revealed a significant treatment effect between MBAT and CBT (MBAT vs. CBT: OR = 4.34, 95% CI: 1.35 to 13.99, p = .014, Effect Size = .81) and between MBAT and UC (MBAT vs. UC: OR = 4.82, 95% CI: 1.25 to 118.57, p = .023, Effect Size = .87; see Figure 3).
Figure 3. Seven-day point prevalence abstinence rates by treatment group at 4 and 26 weeks post quit day among individuals classified as smoking on the last treatment session (intent to treat). N=151. See the online article for the color version of this figure.
Associations of Mindfulness Practice Dosage During Treatment With Abstinence
Among individuals randomized to MBAT, self-reported mindfulness practice during treatment was examined (see Table 2). Specifically, six items assessed the average number of days over the previous week spent practicing the following activities: sitting meditation, body scan, walking meditation, yoga, awareness of the breath during the day, and the exercises in the workbook. These six items were administered at seven times points (i.e., at treatment Sessions 2 through 8). For each mindfulness practice technique at each time point, the association with abstinence was examined at the end of treatment, 4 weeks following the quit day, 26 weeks following the quit day, and over time (i.e., a total of 4 analyses for each of the 6 items). This resulted in 168 statistical comparisons (i.e., 6 practice technique items × 7 assessment time points × 4 abstinence measures = 168 tests of association). Analyses yielded six significant findings encompassing four different constructs out of 168 tests. Given that there are actually fewer significant results than would be expected by chance, and the fact that the significant results were not consistent with respect to identifying particular constructs or patterns that might be important, we do not report these results.
Average Self-Reported Days Spent Practicing MBAT Techniques Across the 8 Weeks of Treatment Among Individuals Randomized to MBAT
Association of Prior Meditation Experience With Abstinence
Associations between experience with meditation prior to study entry and abstinence were also examined. Results indicated that experience with meditation was not associated with smoking abstinence in the overall sample (ps > .184), and previous experience with meditation did not interact significantly with treatment condition to predict smoking abstinence (ps > .221). Among participants classified as smoking at the last treatment session, those who had (vs. did not have) previous experience with meditation were more likely to recover abstinence from smoking across the two follow-up assessment points (OR = 3.61, 95%: 1.21 to 10.74, p = .022, Effect Size = .71 for completers and OR = 3.34, 95%: 1.16 to 9.58, p = .025, Effect Size = .67 for intent-to-treat analyses). However, previous experience with meditation was not found to interact with treatment condition to predict abstinence recovery among individuals classified as smoking on the last treatment session (ps > .860). Finally, associations between total number of treatment sessions completed and smoking abstinence were examined. The analyses examining this association with smoking abstinence over time or in single time point analyses were not statistically significant (all ps ≥ .051).
Attrition and Sensitivity Analyses
Differences in abstinence rates at 4 weeks post quit day were examined between participants with complete data versus those lost to follow-up on demographics (age, gender, race/ethnicity, education, income, marital status, employment status), nicotine dependence, psychosocial factors (perceived stress, negative affect, positive affect, history of depression), and treatment group. No significant differences were found. Similarly, with regard to differences in attrition rates by treatment group, a chi-square analysis indicated that there were no significant differences, χ(2)2 = 2.738, p = .254, when examined 4 weeks following the quit day (MBAT = 6.8%; ST = 6.6%; UC = 6.3). However, participants with complete data and those with missing data at 26 weeks post quit day differed significantly in race/ethnicity (p = .004), marital status (p = .027), and positive affect at baseline (p = .037). Those lost to follow-up at 26 weeks post quit day were more likely to be non-Hispanic White (as opposed to African American), married or living with a partner, and have lower positive affect scores. Consistent with the examination of treatment group differences at 4 weeks post quit day, there were no significant differences in attrition rates by treatment group at the 26-week assessment (MBAT = 12.4%; ST = 13.1%; UC = 9.7%), χ(2)2 = 0.899, p = .638. Associations between perceived stress, negative affect, and positive affect at 4 weeks post quit day, and missingness at 26 weeks post quit day, were examined. No significant associations were found.
To address potential bias arising from missing data, sensitivity analyses were conducted to examine the effect of varying missing data assumptions using a multiple imputation approach for treatment effects on 7-day point prevalence abstinence outcomes. Pattern-mixture models were used to generate inferences for various scenarios under the MNAR assumption, with a shift parameter chosen as 0.5, 1, 5, −0.5, −1, or −5 to reflect different degrees of departure of the missing data mechanism from MAR (page 5100, chapter 63: The MI procedure, SAS 9.4 documentation). Results obtained from separate analyses of MBAT versus CBT, and MBAT versus UC, using the multiple imputation of treatment effects on 7-day point prevalence abstinence were similar to completers only or intent-to-treat analysis approaches (details of those nonsignificant results are not shown). The conclusions obtained under the missing not at random (MNAR) assumptions were similar to the ones under missing at random (MAR) in that the nonsignificant results remained the same. Therefore, we are confident that our study findings of treatment effects on 7-day point prevalence abstinence outcomes were robust.
Significant findings supporting the analyses examining the effect of MBAT in facilitating recovery from a lapse (MBAT vs. CBT) remained the same across all methods: multiple imputation, intent-to-treat, and completers only (OR = 3.22 to 5.01, p value = 0.010 to 0.020, see table below). However, the MBAT versus UC analysis result was slightly different in the multiple imputation approach (OR = 2.72, p = .123) compared with the other two methods (intent-to-treat OR = 4.82, p = .023, completers only: OR = 4.18, p = .043). Sensitivity analyses of the lapse recovery results (MBAT vs. UC) using multiple imputation with the MNAR assumption revealed similar results compared with the MAR assumption. Therefore, the significant finding based on the ITT analysis (missing = relapsed) in this case needs to be interpreted with caution.
DiscussionThis randomized clinical trial was designed to evaluate the efficacy of MBAT compared to CBT and UC with respect to both smoking cessation and recovery from a lapse. Results indicated that there were no significant overall differences in abstinence rates across the three treatments. The results were surprising for several reasons. First, several recent randomized controlled trials have indicated that mindfulness-based treatments for tobacco dependence improve abstinence outcomes compared to standard smoking cessation treatments (Brewer et al., 2011; Davis, Goldberg et al., 2014). Second, both MBAT and CBT were more intensive therapies than was UC, and treatment intensity has been strongly associated with greater efficacy (Fiore et al., 2008). However, MBAT did show benefits over and above CBT and UC in promoting recovery from a lapse, consistent with findings on the efficacy of MBRP for relapse prevention among individuals with substance use disorders (Bowen et al., 2014). Specifically, among participants who were not abstinent at the end of treatment, those randomized to MBAT appeared to be more likely to recover abstinence post treatment. Thus, although MBAT did not produce superior abstinence rates compared to UC or CBT, MBAT may be effective for preventing early lapses from transitioning to full-blown relapse.
There may be several potential reasons why we failed to find a significant effect of MBAT over the control conditions on abstinence. The 7-day point prevalence abstinence rate for our MBAT group 1 week posttreatment using an intent-to-treat approach was very similar to that found by both Brewer and colleagues (2011) and Davis, Goldberg et al. (2014) at the end of treatment (i.e., 38% in MBAT, 36% in the MT trial conducted by Brewer, and 25.7% in the MTS trial conducted by Davis). However, abstinence rates in our two control groups at the end of treatment were substantially higher than those observed in the Brewer and Davis trials (i.e., 38.1% in CBT and 27.2% in UC 1 week post treatment compared to 15% at the end of treatment in Brewer et al. (2011) and 17.6% at the end of treatment in Davis, Goldberg et al. (2014). Our comparison of MBAT to CBT was also extremely rigorous given that: (a) CBT represents the current state of the science approach, and (b) CBT and MBAT were matched on treatment duration, contact time, and therapists. Nevertheless, MBAT did not improve overall cessation rates as hypothesized. Another possibility is that the MBAT intervention may have unintentionally reduced nicotine patch use relative to the other two treatments. Such a scenario could potentially have led to an overall failure to find overall treatment group differences.
The finding that MBAT appeared to improve lapse recovery is consistent with theoretical and empirical work on mindfulness. Specifically, mindfulness is hypothesized to promote a “decentered perspective,” which reduces the tendency for automatic emotional reactions, and this enhanced emotional regulation is, in turn, thought to attenuate the likelihood of relapse. Mindfulness is also thought to moderate the association between negative affect and relapse such that in the face of negative affect, individuals with higher levels of mindfulness should have a lower likelihood of relapse compared to individuals with lower levels of mindfulness (i.e., the linkage between negative affect and relapse is weakened among individuals with higher levels of mindfulness; Roemer & Orsillo, 2003; Teasdale, 1997; Teasdale et al., 2002). Recent research has been supportive of both effects with respect to relations among mindfulness, negative affect, and alcohol problems (i.e., that mindfulness both reduces negative affect and reduces the strength of the association between negative affect and alcohol problems; Adams et al., 2014). Neurological studies also provide support that mindfulness training reduces both the severity of negative emotions and reactivity to those emotions (Brown, Goodman, & Inzlicht, 2013; Farb, Anderson, & Segal, 2012; Goldin & Gross, 2010; van den Hurk, Janssen, Giommi, Barendregt, & Gielen, 2010). Thus, MBAT may have improved recovery from a lapse by lessening the negative emotional response to a lapse, and/or by weakening the association between the negative emotional response to a lapse and the likelihood of future lapses.
The fact that MBAT may have some promise in helping smokers recover from early lapses has important implications given that existing treatments designed to prevent relapse and promote recovery from lapses have generally not demonstrated superior efficacy relative to other treatment approaches (Carroll, 1996; Lichtenstein & Glasgow, 1992). Our results suggest that incorporating mindfulness-based techniques into existing smoking cessation treatments could potentially improve the recovery of abstinence after lapses. For example, treatments that increase mindfulness might simply lessen the impact of a lapse when it does occur as noted above, and it also possible that mindfulness strategies could be strategically employed in response to lapses. In particular, briefer meditative practices and other “nonmeditation” mindfulness practices might be well-suited to acute lapse-recovery situations. Other possibilities are that mindfulness-based interventions might be particularly effective for more recalcitrant smokers who are likely to lapse early in a quit attempt, or that such interventions could improve cessation rates over a longer course of time in which smokers make multiple quit attempts, lapse, and attempt to regain abstinence. In addition, researchers have suggested that smokers with high anxiety sensitivity related to mental concerns (e.g., fear that having difficulty concentrating means that one is going crazy) might particularly benefit from mindfulness training (Guillot, Zvolensky, & Leventhal, 2015). Finally, MBAT may have important utility as a relapse-prevention intervention that is delivered after the achievement of initial abstinence from smoking. That is, mindfulness practice may have particular efficacy in mitigating the impact of lapses leading to full-blown relapse as opposed to facilitating initial cessation success. Further research evaluating the efficacy of mindfulness-based techniques in relapse prevention/recovery is warranted, as is research examining whether such approaches are particularly effective for certain people.
It is unclear why mindfulness practice was not related to overall abstinence in the current study. However, formal mindfulness practice rates were low, and the association of mindfulness practices with cessation could have been attenuated by a restriction in range in the practice variables. Along these lines, it may simply be that a greater amount of mindfulness practice that occurs outside treatment sessions is needed to meaningfully impact cessation outcomes. Another possibility is that our measures of mindfulness practice were crude and may not accurately capture the amount of practice, and they did not capture the quality of practice, which may be essential. In addition, more informal mindfulness practices that occur throughout the day (e.g., 3-min breathing space, mindful attention to thoughts or feelings) were not assessed, and these more in-the-moment practices may be important in influencing cessation outcomes.
A marked strength of the current study was the inclusion of two control groups representing different levels of treatment intensity. Our CBT treatment was delivered in a group format and matched the MBAT treatment on contact time and intensity. Our UC group was delivered individually and was comparable to standard Guideline-based treatment that might be delivered in the community. Our use of the same two therapists to deliver the three treatment conditions in the current study was an important study design consideration. We chose to use the same therapists to help ensure that any potential differences that emerged between the treatment conditions would be attributable to the treatment rather than to therapist characteristics.
Another considerable strength is our community-based sample. Participants were racially/ethnically diverse, relatively low-income, just over half were female, and more than two thirds were without a partner. The current findings indicate that MBAT yielded similar abstinence rates compared to more traditional Guideline-based treatments among a diverse and relatively low SES sample of smokers, suggesting that MBAT may be a viable treatment option for such individuals.
Some important limitations should also be acknowledged. Given that MBAT requires specialized and intensive training on the part of therapists and a high level of engagement on the part of individuals enrolled in the treatment, MBAT is not likely to be broadly disseminable in its current format. This is an important limitation from a public health perspective, and a critically important goal for future research should be to examine ways to enhance the disseminability of mindfulness-based strategies. Second, it is important to acknowledge that the use of the same two therapists to deliver all of the study treatments may have resulted in a phenomenon known as “treatment diffusion bias” (Kazdin, 1992). Treatment diffusion bias threatens internal validity, and this phenomenon may have contributed to the absence of significant differences between treatment conditions in the current study. One potential mechanism that may have contributed to treatment diffusion bias is the warmth and compassion expressed by therapists proficient in the delivery of mindfulness-based interventions. For example, modeling of self-compassion may have occurred in all three treatment conditions and may have served as a mechanism facilitating cessation. Such modeling has been suggested to be an active ingredient in mindfulness-based treatments (van der Velden et al., 2015).
A third important limitation is a lack of data to establish fidelity by study interventionists. The treatments were manualized and included specific checklists of topics and activities for each therapy approach and each session. MBAT included very specific activities that were major components of treatment with respect to both content and time spent in therapy that were clearly not part of the CBT or UC treatments. Thus descriptively the interventions differed in important and meaningful ways. However, interventionists were not rated for fidelity to each intervention. In addition, the assessment of treatment fidelity decreases the likelihood of treatment diffusion bias. Thus, the absence of treatment fidelity assessment is an important study limitation. Future research should incorporate ratings to establish that MBAT is conducted with fidelity. A fourth limitation is that rates of compliance with formal meditative practices were low in the current study. Thus, strategies that increase the acceptability of meditation-based practices, or the inclusion of more acceptable nonmeditation practices, are clearly needed when reaching out to the general population of smokers. A fifth limitation is that information on use of the nicotine patch was not collected during the study. Because patch use was not tracked, it was not possible to examine potential interactions between MBAT, patch use, and abstinence. A sixth limitation is that our definition of “lapse recovery” among individuals who were smoking at the end of treatment did not differentiate between individuals who never quit versus those individuals who achieved some period of abstinence during the treatment period. Finally, participant attrition is an important study limitation that should be acknowledged.
In summary, the results of this large RCT, at least with respect to comparison with other mindfulness-based treatment studies, indicate that MBAT yielded abstinence rates that were similar to two standard Guideline-based treatments of varying intensity among a diverse and relatively low SES sample of smokers. Furthermore, compared to the two control conditions, MBAT may have greater efficacy than CBT and UC in helping individuals recover from lapses. This finding has both clinical and theoretical implications, and future research should examine both replicability and the mechanisms underlying this effect. Future studies should also examine the efficacy of “booster” treatment sessions delivered during the follow-up period. Investigating the efficacy of mindfulness treatment approaches that do not utilize meditation as a primary technique is another important direction for future research. Finally, given that the population of remaining smokers appears to be becoming increasingly recalcitrant (Irvin & Brandon, 2000; Irvin, Hendricks, & Brandon, 2003), specialized, intensive treatments such as MBAT are likely to be needed for certain subgroups of smokers who may have particular difficulty quitting. As such, studies should examine individual differences as potential moderators of the efficacy of MBAT.
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Submitted: December 31, 2014 Revised: March 8, 2016 Accepted: April 8, 2016
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Source: Journal of Consulting and Clinical Psychology. Vol. 84. (9), Sep, 2016 pp. 824-838)
Accession Number: 2016-25468-001
Digital Object Identifier: 10.1037/ccp0000117
Record: 18- Title:
- Examining a dimensional representation of depression and anxiety disorders' comorbidity in psychiatric outpatients with item response modeling.
- Authors:
- McGlinchey, Joseph B.. Department of Psychiatry and Human Behavior, Brown University, Providence, RI, US, jmcglinchey@lifespan.org
Zimmerman, Mark. Department of Psychiatry and Human Behavior, Brown University, Providence, RI, US - Address:
- McGlinchey, Joseph B., Rhode Island Hospital-Bayside Medical Building, 235 Plain Street, Suite 501, Providence, RI, US, 02864, jmcglinchey@lifespan.org
- Source:
- Journal of Abnormal Psychology, Vol 116(3), Aug, 2007. pp. 464-474.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- comorbidity, item response models, DSM-V, dimensional versus categorical approaches, major depression
- Abstract:
- The current study replicated, in a sample of 2,300 outpatients seeking psychiatric treatment, a previous study (R. F. Krueger & M. S. Finger, 2001) that implemented an item response theory approach for modeling the comorbidity of common mood and anxiety disorders as indicators along the continuum of a shared latent factor (internalizing). The 5 disorders examined were major depressive disorder, social phobia, panic disorder/agoraphobia, specific phobia, and generalized anxiety disorder. The findings were consistent with the prior research. First, a confirmatory factor analysis yielded sufficient evidence for a nonspecific factor underlying the 5 diagnostic indicators. Second, a 2-parameter logistic item response model showed that the diagnoses were represented in the upper half of the internalizing continuum, and each was a strongly discriminating indicator of the factor. Third, the internalizing factor was significantly associated with 3 indexes of social burden: poorer social functioning, time missed from work, and lifetime hospitalizations. Rather than the categorical system of presumably discrete disorders presented in DSM-IV, these 5 mood and anxiety disorders may be alternatively viewed as higher end indicators of a common factor associated with social cost. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Anxiety Disorders; *Comorbidity; *Major Depression; Diagnostic and Statistical Manual; Item Response Theory; Outpatients
- Medical Subject Headings (MeSH):
- Adult; Anxiety Disorders; Comorbidity; Depressive Disorder, Major; Diagnostic and Statistical Manual of Mental Disorders; Female; Humans; Male; Mental Disorders
- PsycINFO Classification:
- Psychological Disorders (3210)
- Population:
- Human
Male
Female
Outpatient - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs) - Tests & Measures:
- University of Michigan's Composite International Diagnostic Interview
Structured Clinical Interview for DSM-IV - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Jan 25, 2007; Revised: Jan 16, 2007; First Submitted: Jul 21, 2006
- Release Date:
- 20070813
- Copyright:
- American Psychological Association. 2007
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/0021-843X.116.3.464
- PMID:
- 17696702
- Accession Number:
- 2007-11737-003
- Number of Citations in Source:
- 43
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2007-11737-003&site=ehost-live">Examining a dimensional representation of depression and anxiety disorders' comorbidity in psychiatric outpatients with item response modeling.</A>
- Database:
- PsycINFO
Examining a Dimensional Representation of Depression and
Anxiety Disorders' Comorbidity in Psychiatric Outpatients
With Item Response Modeling
By: Joseph B. McGlinchey
Department of Psychiatry and Human Behavior,
Brown University;
Mark Zimmerman
Department of Psychiatry and Human Behavior,
Brown University
Acknowledgement:
The current system
of the Diagnostic and Statistical Manual of Mental
Disorders (4th ed.,
DSM–IV; American Psychiatric Association [APA],
1994) is based on a neo-Kraepelinian model
in which mental disorders are conceptualized and presented as
discrete entities. In the DSM–IV,
mental disorders are grouped together by rationally derived
classes on the basis of shared phenomenological features.
However, the current categorical DSM system,
which presumes distinct mental disorders with little overlap,
has been the subject of criticism, controversy, and debate
(Beutler &
Malik, 2002; Widiger & Clark,
2000). The potential benefits of
incorporating a dimensional system of measurement for assessing
mental pathology was the focal point of the November 2005 issue
of the Journal of Abnormal Psychology
(Krueger, Watson,
& Barlow, 2005). One of the main
limitations of the current categorical system of the
DSM concerns the issue of diagnostic
comorbidity, the co-occurrence of different disorders within the
same individual (Widiger & Samuel, 2005). The
observation of comorbidity rates greater than would be expected
by chance has remained a significant challenge to establishing a
sound nosology on which to base clinical assessment and
treatment. Such a large degree of diagnostic overlap suggests
the presence of shared core processes underlying supposedly
distinct disorders.
Substantive rates
of comorbidity have been especially well documented among mood
and anxiety disorders (Kessler et al., 1996;
Maser &
Cloninger, 1990; Mineka, Watson, & Clark,
1998; Zimmerman, Chelminski, & McDermut,
2002). As a result, explanatory models
attempting to account for mood and anxiety comorbidities have
gained prominence. Watson (2005) recently detailed the
history of such structural models. To briefly summarize, there
is evidence suggesting two dominant dimensions of affective
experience: a higher order dimension of negative emotion
representing subjective distress and dissatisfaction (negative
affect) as well as another dimension (positive affect)
reflecting co-occurrences among positive mood states
(Watson &
Clark, 1984; Watson & Tellegen,
1985). It was proposed that negative
affectivity represents a nonspecific factor common to both
depression and anxiety, whereas positive affectivity is a factor
specific to depression in that it exhibits more consistent
negative correlations with depression relative to anxiety. This
two-factor model was later expanded to a tripartite model
(Clark &
Watson, 1991) that included a third
factor of hyperarousability more specific to anxiety. Subsequent
modeling has suggested limitations of this third arousal factor
in accounting for the heterogeneity within anxiety disorders and
evidence that positive affectivity also exhibits consistent
negative associations with social phobia (Brown, Chorpita, & Barlow,
1998; Watson, Clark, & Carey,
1988). In light of these findings, an
integrative hierarchical model (Mineka et al., 1998) has been
advanced in which each individual mood and anxiety syndrome
contains a shared component (i.e., the common, higher order
factor of negative affectivity) and a unique component of
features that distinguish the disorder from others. In related
factor analytic work, Krueger (1999) determined that a
three-factor model best fit the data for describing the common
mental disorders of the
DSM–III–R
(American Psychiatric
Association, 1987). Two of these were
strongly correlated subfactors: anxious–misery,
consisting of major depression, dysthymia, and generalized
anxiety disorder (GAD); and fear, consisting of panic disorder,
agoraphobia, social phobia, and simple phobia. These two
subfactors composed a higher order internalizing factor that was
more modestly correlated with a higher order third factor,
externalizing, consisting of alcohol dependence, substance use
dependence, and antisocial personality disorder.
These structural
modeling studies challenge certain assumptions and
categorizations found in the current DSM. For
example, though the DSM rationally categorizes
GAD as an anxiety disorder, these studies suggest that the
diagnosis of GAD is actually more strongly correlated to
depression than it is with other anxiety disorders, with both
strongly loading onto a common factor of distress or misery.
Accordingly, a quantitative hierarchical system recently has
been advanced (Clark
& Watson, 2006;
Watson &
Clark, 2006), calling for a
reorganization of the current presentation of the
DSM. As an alternative to the
DSM system of grouping diagnoses on the basis
of perceived shared features, the hierarchical system supports
empirically based classification more accurately reflecting the
clusters of covariation among the disorders. In this system,
unipolar mood and anxiety disorders would be placed under a
general class of internalizing disorders, further subdivided
into two subclasses representing distress (i.e., major
depression, dysthymia, GAD, and posttraumatic stress disorder)
and fear (panic disorder, agoraphobia, and social and specific
phobias) disorders, respectively.
Contemporary
psychometric approaches have begun to play an important role in
informing alternative dimensional conceptualizations of
diagnostic co-occurrence. One such approach is the use of models
based on item response theory (IRT; Embretson & Reise,
2000). Item response modeling (IRM) refers
to a class of psychometric procedures that can be used to
quantitatively scale a set of observed indicators along a
dimensional continuum representing an underlying latent
construct. IRM conveys meaning in terms of trait level (i.e.,
the degree of the latent construct being measured) and in the
properties of the items used to represent the construct. The
dimensional continuum representing the latent construct is
scaled logistically or normally, expressed either in terms of
logit or standard deviation units ranging from –3.0 to
3.0. In applying IRM to the issue of mood and anxiety
comorbidity, if one administers a diagnostic interview to assess
depression and anxiety disorders and if substantive comorbidity
is observed linking these diagnoses together, then this should
be reflected by significant positive correlations among all of
the disorders. An appropriate model can then be applied to the
data so that one can assess how the diagnostic indicators
contribute to the measurement of the unobservable construct
underlying them.
To date,
Krueger and Finger
(2001) have performed the most extensive
study applying IRM to examine the comorbidity of unipolar
depression and anxiety disorders. Their data were drawn from the
National Comorbidity Survey (NCS; Kessler et al., 1994), one of
the largest national probabilistic studies undertaken to
ascertain the prevalence and comorbidity of psychiatric
disorders among the general U.S. population. Their study
examined a weighted subsample consisting of 251 NCS participants
who indicated that they had sought treatment for mental health
problems in the past year by affirmatively responding to the
question, “Are you currently seeing any professional
about your problems [with emotions, nerves, alcohol, or
drugs]?” (Krueger & Finger, 2001, p.
143). NCS participants were interviewed by nonclinicians trained
in administration of the University of Michigan's Composite
International Diagnostic Interview (UM–CIDI;
Wittchen &
Kessler, 1994). Krueger and Finger's
study consisted of three phases. In the first phase, they showed
that the observed comorbidity among lifetime diagnoses of seven
unipolar mood and anxiety disorders could be sufficiently
accounted for in terms of a common shared latent factor,
internalizing. The diagnoses they analyzed were major depressive
episode, GAD, social phobia, simple phobia, panic disorder,
agoraphobia, and dysthymia. In the second phase, having
established evidence for the tenability of a common
internalizing factor underlying the seven diagnoses, they used
IRM to examine how each of these diagnostic indicators mapped
along a continuum of internalizing. They found that, with the
possible exception of dysthymia, each of the seven diagnoses was
a strong indicator of internalizing and that they were
individually located at the upper half of the internalizing
continuum. When combined, the diagnoses measured the upper half
of the internalizing continuum with greater precision than did
the lower half. In the third phase of the study, Krueger and
Finger focused on validating the internalizing factor by
examining its association with two criteria of social burden:
the number of lifetime inpatient admissions that participants
had experienced because of a mental disorder and the number of
days in the past month that the participants' functioning had
been impaired because of a mental disorder. They found that
participants' trait estimates of internalizing were almost
perfectly correlated with a simple count of number of diagnoses
and that those participants who exhibited the greatest degree of
internalizing (i.e., meeting criteria for six or seven of the
disorders) had a significantly greater number of
hospitalizations and days of impaired functioning compared with
those with a lesser degree of internalizing (i.e., five or fewer
disorders).
In recognition of
the importance of replication in the ongoing debates concerning
the potential limitations of the categorical
DSM system and diagnostic comorbidity, we
attempted in the current study to reproduce the findings of
Krueger and Finger
(2001), this time applying their
analyses directly to a psychiatric outpatient population.
Specifically, the current study sample was 2,300 outpatients
seeking psychiatric treatment, corresponding with Krueger and
Finger's extraction of a treatment-seeking subsample from the
general community population of the NCS epidemiological data.
The use of a treatment-seeking outpatient sample is advantageous
for IRM, in that it provides for comparatively larger base rates
of psychiatric diagnoses relative to populations that include a
large percentage of individuals without psychiatric disorders.
In contrast to the NCS data, diagnoses for the current study
were established by trained and reliable clinician raters using
the semistructured Structured Clinical Interview for
DSM–IV (SCID; First, Spitzer, Gibbon, &
Williams, 1995).
We tested several
hypotheses based on the prior study. First, we hypothesized that
the comorbidity between unipolar depression and anxiety
disorders would result in significant positive correlations
among all of the diagnostic indicators and could be adequately
represented in terms of one shared higher order internalizing
factor. Second, we hypothesized that when mood
and anxiety disorders were fitted with the same unidimensional
item response model, these disorders each would be located
individually in the upper half of the internalizing continuum,
would be strong discriminators of internalizing, and when
combined would more accurately measure the upper end of the
internalizing continuum. Finally, we hypothesized that the
internalizing factor would show criterion validity as evidenced
by significant associations with three external criteria
indicative of social cost: patients' current social functioning,
missed days of work in the past 5 years, and number of lifetime
psychiatric inpatient admissions.
Method Sample
The current
data were taken from the Methods for Improving Diagnostic
Assessment and Services (MIDAS) project at the Department of
Psychiatry at Rhode Island Hospital. To the best of our
knowledge, the MIDAS project is the largest clinical
epidemiological study in which semistructured interviews are
used to assess a wide range of psychiatric disorders in a
general clinical outpatient practice (Zimmerman,
2003). Among the strengths of the
project are that diagnoses are based on reliable and valid
procedures used in research studies and that patients are
presenting to a community-based psychiatric outpatient
practice rather than to a specialized clinic focusing on the
research and treatment of one or few disorders. This private
practice group predominantly treats individuals with medical
insurance (including Medicare but not Medicaid) on a
fee-for-service basis.
To date, 2,300
psychiatric outpatients have been evaluated with the
semistructured diagnostic interview. Table 1 lists the sociodemographic
characteristics of the current sample in comparison with the
sample used by Krueger and Finger (2001). The
majority of the sample was female (60.5%), European American
(87.6%), and married or cohabiting (46.2%). The mean age of
the sample was 38.2 years (SD = 12.8).
Demographic Characteristics and Comparison With
Krueger and Finger's
(2001) Sample
Procedure and Assessment of Psychiatric Diagnoses
Greater detail
on the specific procedures and assessment of the MIDAS
project can be found elsewhere (Zimmerman,
2003). Briefly, patients were invited to
participate in a clinical study and provided written
informed consent. The research protocol was approved by the
institutional review board of the hospital. Patients were
then interviewed at their intake evaluation by a diagnostic
rater who administered the SCID. Diagnostic raters included
PhD-level psychologists and research assistants with college
degrees in the social or biological sciences. Research
assistants received 3–4 months of training in
which they observed at least 20 interviews and additionally
were observed and supervised in administering more than 20
evaluations. Psychologists observed 5 interviews and were
observed and supervised in administering 15–20
evaluations. During the course of the training, a senior
diagnostician with established reliability met with each
rater to review the interpretation and rating of every item
on the SCID. At the end of the training period, raters were
required to demonstrate exact, or nearly exact, agreement
with a senior diagnostician on 5 consecutive SCID
administrations. Throughout the MIDAS project, ongoing
supervision of raters consisted of weekly diagnostic case
conferences involving all members of the team, and item
ratings of every case were reviewed by the lead
diagnostician.
Data Analysis
In order to
maximize comparability, we analyzed the data of this study
adhering as closely as was possible to the methods of
Krueger and
Finger (2001), including use of the
same modeling, correlational indices, and estimation
procedures applied to the data. However, we should first
outline some important distinctions between the studies.
This study concerns analyses of current diagnoses, because
these are generally the reason for presentation in routine
clinical outpatient settings, whereas Krueger and Finger's
study examined lifetime diagnoses. As an epidemiologic
instrument geared toward the general population, the
UM–CIDI is structured to first assess for the
lifetime presence of disorders; initial probes for core
symptoms of disorders are placed together at the beginning
of the interview, and time of occurrence of a disorder is
established after diagnostic criteria have been confirmed.
For the MIDAS SCID, the initial probes are assessed for
current MDD and GAD but are assessed at the lifetime level
for the remaining anxiety disorders. Another difference
between instruments is that the MIDAS SCID applies criteria
from the DSM–IV for diagnoses,
whereas the UM–CIDI applies criteria from the
DSM–III–R. Both
instruments use comparable skipout placements for the
included disorders.
Krueger and
Finger's (2001) analysis of the
diagnoses did not take into account the hierarchical,
exclusionary rules outlined by the DSM for
making diagnoses (Zhao, Kessler, & Wittchen,
1994), whereas the MIDAS project's
assignment of the diagnoses did adhere to
DSM exclusionary rules. This difference
would be expected to decrease comorbidity rates in our data
relative to the NCS data. For example, a patient endorsing
diagnostic criteria for GAD occurring exclusively within MDD
would be assigned a diagnosis of modified GAD
(Zimmerman
& Chelminski, 2003) in the
MIDAS project, in keeping with the specifications of the
DSM, whereas the same patient would be
diagnosed as having GAD in Krueger and Finger's study in
which hierarchical rules were suspended. In the prior IRM
study, panic disorder and agoraphobia were captured under
two diagnostic indicators representing each of the
phenomena, whereas in the MIDAS project, the three
DSM categories are specified for these
phenomena: panic disorder with agoraphobia, panic disorder
without agoraphobia, and agoraphobia without panic disorder.
To accommodate these differences, we included diagnoses of
modified GAD together with GAD and also combined the three
DSM diagnoses for panic and agoraphobia
disorders into one diagnostic indicator. Finally, we chose
not to include dysthymic disorder in the current study. The
SCID provides a more stringent assessment of current
dysthymic disorder, which in the interview sequentially
follows the assessment of MDD. In the SCID's reflection of
the DSM's exclusionary rules, the attempt
is to maximally partition dysthymic disorder from MDD. In
the SCID, if a patient had already met criteria for current
MDD, as was frequent, then the presence of current dysthymia
was subsequently assessed by querying about depressed mood
for the 2-year period preceding the onset of the current
major depressive episode (i.e.,
“double-depression”). Relative to
Krueger and Finger's study in which DSM's
hierarchical rules were suspended, we expected this to
result in lower base rates of current dysthymic disorder and
a weaker degree of correlation between dysthymic disorder
and MDD. Supporting this, the rate of endorsement for
current dysthymia in our sample was 7.1% versus 22.1% in
Krueger and Finger's rate, and a measure of association for
observed counts of two dichotomous variables yielded
practically no association between MDD and dysthymic
disorder (φ = .03, p >
.05). Because of this assessment constraint, we judged that
the inclusion of dysthymic disorder would produce more noise
than signal and that MDD alone would serve as a sufficient
representation of unipolar depression.
In summary,
five dichotomous indicators were analyzed representing the
presence or absence of current MDD (corresponding to major
depressive episode), panic disorder/agoraphobia, specific
phobia (corresponding to simple phobia), social phobia, and
GAD. As an ongoing part of the MIDAS project,
joint-interview diagnostic reliability information was
collected on 48 participants. For disorders diagnosed in at
least 2 patients by at least one of the two raters, the
kappa coefficients were as follows: MDD, 0.91; panic
disorder, 1.0; social phobia, 0.84; specific phobia, 0.91;
and GAD, 0.93.
Like
Krueger and
Finger (2001), we applied the
two-parameter logistic (2PL) model using marginal maximum
likelihood (Bock
& Aitkin, 1981) estimation
in order to ascertain how the five diagnostic indicators
mapped onto the underlying internalizing factor. The 2PL is
a unidimensional item response model that assumes one,
common latent trait can account for the interrelationships
among dichotomous indicators. To test the assumption of
unidimensionality, we fit a one-factor confirmatory factor
analysis (CFA) to the data using Mplus 3.0
(Muthén & Muthén,
2004). To perform a CFA with dichotomous
indicators, we used weighted least squares mean- and
variance-adjusted estimation, in which a diagonal weight
matrix is applied to obtain parameter estimates and a full
weight matrix to obtain mean- and variance-adjusted standard
errors and chi-square test statistics. Weighted least
squares estimation requires the use of an asymptotic
correlation matrix, and therefore we used the tetrachoric
correlation as the appropriate index of association between
diagnostic indicators.
The two
parameters of the 2PL model are item difficulty and item
discrimination. Difficulty refers to the location
of each item (i.e., diagnosis) along the dimensional
continuum. When trait level (i.e., degree of internalizing)
is equivalent to the item difficulty parameter for the
diagnosis, there is a 50% likelihood of positive
endorsement. In this context, difficulty reflects the
likelihood of meeting the criteria for the diagnostic
indicator. A patient who would have a 50% chance of
positively endorsing a diagnosis with a large difficulty
parameter would have an even greater likelihood of endorsing
diagnoses of comparatively lower item difficulty. The
discrimination, or slope parameter, refers to how sharply
each diagnostic indicator differentiates individual
differences at all locations along the dimensional
continuum. The greater the discrimination parameter, the
more information the item contributes toward the latent
construct.
IRM also
provides a score, or latent trait estimate, that corresponds
to patients' estimated degree of internalizing. Because the
2PL model allows item discrimination parameters to vary
across indicators, a patient's estimated degree of
internalizing depends on his or her specific pattern of
endorsement for the set of disorders. Two different response
patterns can yield variable trait-level scores despite the
same number of diagnoses being endorsed, with disorders of
greater discrimination leading to higher scores. As in the
Krueger and Finger study, we used expected a posteriori
(EAP; Bock &
Aitkin, 1981) latent trait estimates
for the degree of internalizing for each patient,
corresponding to each of the 32 possible diagnostic patterns
for the five disorders. An advantage of EAPs is that they
accommodate perfect response vectors (i.e., those cases
where patients met criteria for all five disorders or for no
disorders).
We conducted
2PL analyses using MULTILOG (Version 7.0; Thissen, Chen, & Bock,
2002). In MULTILOG, log metric
scaling is used to fit the 2PL model (i.e., the latent trait
continuum ranges from –3.0 to 3.0 logit units). As
a result of this scaling, item parameters are approximately
1.7 times higher than they would be in a normal (i.e.,
z score) scaling metric. The latent
trait parametrization of IRM can be interpretable with the
common factor parametrization of factor analysis
(McDonald,
1999). For example, the item
discrimination parameter bears a relationship to
standardized factor loadings if the following formula is
used:
where bi represents the item discrimination parameter and
λi represents the item factor loading, respectively (see
Ackerman,
2005, p. 5).
Validating the Internalizing Factor
In the third
phase of Krueger
and Finger's (2001) study, they
assessed the criterion validity of the internalizing factor
common to mood and anxiety diagnoses by examining its
association with two indexes of social cost: number of
lifetime psychiatric hospitalizations and number of days in
the past month that patients were unable to work because of
psychiatric illness. In the current study, we chose three
ordinal variables representing social cost. Each was
negatively valenced, with a larger value having poorer
clinical implications. Two of these were taken from the
Schedule for Affective Disorders and Schizophrenia
(Endicott
& Spitzer, 1978): greatest
level of social functioning achieved in the past 5 years and
amount of missed work in the past 5 years because of
psychopathology. The third variable was number of lifetime
psychiatric hospitalizations, with an upper cutoff of five
or more. For analyses involving time missed from work in the
last 5 years, those patients for whom the item was not
applicable (n = 217) were excluded.
To determine
criterion validity, we examined correlations between EAPs
and the number of diagnoses endorsed and between EAPs and
the three social cost variables. We conducted separate
Kruskal–Wallis tests to examine differences
between groups comprising the number of diagnoses endorsed
on each of the three social cost variables. Patients who met
criteria for four or five of the diagnostic indicators were
collapsed into one category, yielding five total groups of
participants with zero, one, two, three, and four or five
diagnoses. For significant omnibus results, we conducted
post hoc analyses examining each pairwise comparison using
the Mann–Whitney test with Bonferroni adjustment
to control for the 10 comparisons (i.e., p
≤ .005) in each of the three variables.
Results Unidimensionality of the Diagnostic Indicators
Table
2
presents the tetrachoric correlation matrix between the five
diagnostic indicators. As expected, these were all
significantly positively correlated. Goodness-of-fit indices
suggested that the covariation among the five disorders can
be reasonably accounted for in terms of a shared higher
order factor (χ2 = 11.9, p = .04; comparative fit
index = .98; Tucker–Lewis index = .97;
root-mean-square error of approximation = .02). Although
there are no definitive standards for goodness of fit for
dichotomous indicators, under available conventions these
values suggest good fit between the observed data and the
fitted one-factor model (Hu & Bentler,
1999). Figure 1 presents the one-factor CFA model
loadings with parameter coefficients, standard errors (in
parentheses), and residual variances for the diagnoses.
Tetrachoric Correlations for Five Unipolar Mood and Anxiety
Diagnoses (N = 2,300)
Figure 1. One-factor confirmatory model for five unipolar mood and anxiety
disorders, showing parameter coefficients, standard errors (in
parentheses), and residual variances for the diagnoses.
IRT Analysis
Table
3
presents the 2PL parameter estimates for each
diagnosis. All diagnostic indicators were
located in the upper half of the internalizing continuum,
with all item difficulty parameter estimates greater than
0.0, the point representing the average degree of
internalizing for the outpatients. Difficulty parameters are
expected to be above zero when there is a less than 50%
overall endorsement of the diagnosis in the full sample, as
was true for all of the five disorders analyzed. MDD was the
most frequent disorder, with nearly half the sample meeting
criteria for the diagnosis; thus, its difficulty parameter
lies closest to 0.0. The anxiety disorders exhibited
increasingly greater levels of diagnostic difficulty in the
following order: generalized anxiety disorder, social
phobia, panic disorder/agoraphobia, and specific phobia.
Two-Parameter Logistic Item Response Model Parameter Estimates (N
= 2,300)
The five
diagnoses yielded item discrimination parameters greater
than 0.75, suggesting that each acts as a strong contributor
to the underlying internalizing factor. MDD and specific
phobia evidenced a similar degree of discrimination,
followed with increasing discrimination by panic
disorder/agoraphobia, social phobia, and generalized anxiety
disorder, respectively. Applying the previously given
formula to the data in Table 3 and Figure 1, with
1.7 in the numerator to convert between approximating
logistic and probit distributions, shows the relationship
between discrimination parameters and standardized factor
loadings. For MDD, the formula is as follows:
Figure
2
presents the test information function (TIF) and test
standard error of measurement (SEM), which
summarize the combined information of the five diagnoses as
a function of trait level. Information function represents
the squared precision of measurement of the five diagnostic
indicators taken together, whereas the standard error
represents imprecision. The standard error curve is
inversely related to the information function curve. As can
be seen, these vary along the latent trait continuum; IRM
contrasts with classical testing theory, in which standard
error is assumed to be constant regardless of a
participant's score. The diagnoses taken together measured
the higher end of the internalizing continuum with greater
precision than the lower end. The peak of the TIF is the
point at which the five diagnoses combined provides the most
precision in measuring internalizing. The TIF curve for the
diagnoses reached its summit above average internalizing for
the sample (peak = 1.2; SEM = .67). The
marginal reliability, representing the average reliability
across all estimated latent scores, was 0.43.
Figure 2. Test information function graph for internalizing factor. The
solid line represents the test information curve. The total test
information for a specific scale score is read from the left
vertical axis. The dotted line represents the standard error curve.
The standard error for a specific scale score is read from the right
vertical axis.
Validating the Internalizing Factor
Table
4
presents the sample frequency of the 32 possible diagnostic
profiles and their EAP latent trait estimates. EAPs were
nearly perfectly correlated with a count of the number of
diagnoses met (r = .98, p
< .001). EAPs were significantly correlated with
poorer social functioning and greater amount of missed work
(Spearman's ρ = .25 and .24, respectively;
p < .001 for each) and also with
lifetime hospitalizations (Spearman's ρ = .06,
p < .01).
Latent Trait Estimates of Internalizing for 32 Diagnostic
Response Patterns (N = 2,300)
Kruskal–Wallis tests revealed significant
differences overall between the five groups representing
number of diagnoses endorsed on social functioning
(χ2 = 146.6, p < .001), greater
amount of missed work (χ2 = 133.6, p
< .001), and number of lifetime hospitalizations
(χ2 = 12.8, p < .05). For
social functioning, post hoc analyses revealed significant
differences in the direction expected for every pairwise
comparison except two versus three diagnoses and three
versus four or five diagnoses. For missed work, there were
significant differences in the direction expected between
every pair except three versus four or five diagnoses. For
lifetime inpatient psychiatric hospitalizations, the only
significant difference among pairs was between zero versus
three diagnoses.
DiscussionThe current study
replicated the main findings of Krueger and Finger (2001),
with several noteworthy beneficial features. The sample size of
this study was nearly 10 times that of the prior study and also
consisted of a treatment-seeking sample. Our outpatient sample
was drawn from one clinical facility, raising questions of
generalizability. As can be seen from Table 1, despite not
being as nationally representative as the NCS, our sample showed
great comparability to the NCS subsample. In this study, trained
and reliable clinician raters administered the SCID, whereas in
the NCS data analyzed by Krueger and Finger, laypeople
administered a fully structured interview, and it was scored
wholly from participant responses. In the current study, we
maximized comparability with Krueger and Finger by suspending
DSM hierarchical rules wherever possible
and using the same correlational indices, estimation methods,
and model-fitting procedures.
Consistent
findings were obtained for all three phases between studies. As
in the first phase of Krueger and Finger (2001), the five
diagnoses assessed here all exhibited significant positive
correlations with one another, and a one-factor CFA reasonably
accounted for the observed covariation between the five
diagnoses. The 2PL model of this study also exhibited comparable
results to that of Krueger and Finger's second phase. The
ordinal relationship of item difficulty among the diagnoses was
similar: Major depression exhibited the lowest difficulty
estimate and was situated the closest to the average degree of
internalizing in the sample, whereas the remaining anxiety
disorders evidenced increasing thresholds of severity above an
average degree of internalizing. All diagnoses yielded
discrimination parameters greater than 0.75, suggesting that
each is individually responsive to changes among contiguous
levels of internalizing. This was also found for Krueger and
Finger's diagnoses, excepting dysthymic disorder, which was not
assessed here. The TIF curve, representing the combined
information of the diagnostic indicators, peaked at 1.2 units
above average internalizing in the entire sample
(SEM = .67), conforming with Krueger and
Finger's TIF (peak = 1.0, SEM = .57). Finally,
the third phase of the current study was consistent with Krueger
and Finger in showing the criterion validity of the
internalizing factor: Latent trait estimates were significantly
associated with poorer social functioning, greater amount of
missed work, and number of lifetime psychiatric
hospitalizations.
Nonetheless, there
were also differences observed between the studies. The two
largest diagnostic correlations in our data were between social
phobia and GAD (.36) and between MDD and GAD (0.31). In
Krueger and
Finger's (2001) study, these two
pairings were among the lower correlations. Although the
association between simple phobia and major depression fell in
the mid-range of correlations for their study, the corresponding
correlation in our study was clearly the lowest but still
statistically significant. The ordinal relationship of item
difficulty was reversed for three of the anxiety disorders. In
Krueger and Finger, the order in terms of increasing difficulty
was simple phobia, social phobia, and GAD, with simple and
social phobias exhibiting a similar degree of difficulty. In the
current study, the order was GAD, social phobia, and specific
phobia, with GAD and social phobia exhibiting a similar degree
of difficulty. GAD yielded the largest item discrimination in
the current study, whereas it had the lowest discrimination
among these disorders in Krueger and Finger's study.
Because the degree
of endorsement affects the difficulty parameter of a diagnosis
and the degree of noncomorbidity affects how well it coheres to
the common factor, and thus its discrimination, these different
findings serve to highlight considerations in sample and
methodology when IRM is used to model comorbidity. One important
issue concerns the composition of the treatment-seeking samples
in these studies. Our study concerned current psychiatric
diagnoses, whereas the prior study examined lifetime diagnoses.
Relative to current diagnoses, the analysis of lifetime
diagnoses would be expected to increase endorsement for the
disorders and the degree of positive correlation among them.
However, as shown in Table 1, despite lifetime analysis, the
percentage of noncomorbid cases for the NCS subsample was
actually greater across all disorders than in our study; for
social phobia and GAD, it was substantially greater. One
possible explanation for this is that the broad question used to
constitute the NCS subsample may have resulted in inclusion of a
greater number of treatment seekers who required a lower
threshold of mental health care (e.g., being treated by a
primary care physician for one disorder), whereas those
presenting for outpatient psychiatric treatment may have crossed
a higher threshold of mental health care, with greater potential
comorbidity.
The differences in
results may also reflect methodological differences between the
studies. One salient feature is that the modeling of both
studies was based on dichotomous variables that were outcomes of
algorithmic aggregations of symptom-level information from
DSM diagnostic classifications. Dichotomous
categorizations conducted at the diagnostic level have several
limitations (Brown
& Barlow, 2005;
Brown et al.,
1998; Watson, 2005). One limitation
is that the analysis is bound to the nosology that the
categorizations represent. Lifetime GAD endorsement was lower in
the NCS subsample than was endorsement of current GAD in our
sample. The UM–CIDI diagnoses were based on
DSM–III–R criteria,
whereas our SCID diagnoses were based on
DSM–IV criteria. The diagnostic
criteria for the disorders in these IRM studies remained largely
consistent between
DSM–III–R and
DSM–IV, except for GAD. In the
DSM–III–R criteria for
GAD, there were 18 accompanying symptom criteria for excessive
anxiety and worry, of which at least 6 had to be endorsed. The
DSM–IV revision was less
conservative, reducing the accompanying symptom criteria to at
least 3 of 6. This substantial revision in criteria between
DSM editions may have contributed to the
endorsement differences between the two studies, resulting
further in differences in comorbidity and IRM parameter
estimates. The correlation between GAD and MDD in our study is
more consistent with that of Brown et al. (1998), who used
structural modeling to examine dimensions of mood and anxiety
disorders based on DSM–IV criteria.
These dichotomized aggregates also reflect intraedition
DSM inconsistencies in criteria among
disorders (e.g., the 2-week time frame used to establish MDD
versus the 6-month time frame for GAD). A second limitation of
imposing diagnostic dichotomies is that by not reflecting the
dimensional nature of these phenomena, this may lead to the loss
of potentially important clinical information that may be
captured otherwise by continuous measurement. For example, one
criterion necessary in determining the full DSM
diagnosis for these disorders is whether the symptoms cause
significant impairment or distress. This criterion is
particularly pivotal for specific phobia, in which patients
often express fear toward specific stimuli but deny that they
experience significant distress or impairment from their fear.
As such, they do not meet full DSM criteria for
the disorder and would be considered
“absent” for the diagnosis in this study;
their information (features of the disorder) would be lost. A
third limitation of dichotomized diagnostic-level analyses is
that such analyses cannot address the significant heterogeneity
that may still exist within diagnostic labels. For anxiety
disorders, the concern of heterogeneity brought on by multiple
symptom dimensions within disorders is particularly salient for
PTSD, OCD, and specific phobias (Watson, 2005).
Differences in
instrumentation also could have contributed to differences in
findings between the studies. In Table 1, the presence of
specific phobia was only 10.4% in our study versus 28.1% in
Krueger and
Finger's (2001). In its initial probe
for the disorder, the SCID does not explicitly identify some
prominent fears for which the UM–CIDI explicitly
queries (e.g., fear of water and swimming, dentists, storms).
Also, as a structured interview, the UM–CIDI likely
allows a more liberal threshold for assigning the significant
distress or impairment criterion to simple phobias (i.e., merely
responding “Yes” to “Were you ever
very upset with yourself for having this fear?” or
“A lot” to “How much did this fear
ever interfere with your life or activities?”). MIDAS
evaluators using the SCID, in which clarification and further
qualitative assessment of impairment and distress are permitted,
may have a more stringent threshold in rating this criterion.
Despite these
differences, we suspect that reanalysis at the lifetime
diagnostic level would not yield unduly different results
contrasting with the present findings, given the chronic and
persistent nature of depression and anxiety disorders among
treatment seekers. Krueger and Finger (2001) noted in
reanalyzing their data on past-year diagnoses alone that their
results were “essentially identical” (p.
145) to lifetime analyses. Apart from GAD and specific phobia,
the diagnostic endorsement across the two studies in
Table
1 appears reasonably consistent. Still,
confirmation of this is a worthy target of future investigation.
What do the
current findings portend for the publication of
DSM-V? First (2005) argued that the addition
of new subtypes and disorders has increased the clinical utility
of the DSM–IV, although the number of
identified diagnoses increased by 300% between the
DSM–III–R and
DSM–IV (Zimmerman & Spitzer,
2005). Categorical labels hold appeal
because of their communicative efficiency for clinical decision
making (Zimmerman
& Spitzer, 2005); thus, this
trend toward diagnostic proliferation may continue. Given this
possibility, DSM diagnoses at least should be
coherently organized in a manner that optimally groups their
empirical overlap on shared underlying dimensions, lest the
continued arbitrary placement of these into rationally derived
classes leads to an increasing reception of the manual as
dissipated and inconsistent.
The exploration of
a hybrid DSM system in which dimensional
elements are conservatively and gradually integrated, whereas
existing categorical labels and criteria are retained,
represents the most realistic and least disruptive option for
DSM–V. The use of categorical and
dimensional approaches need not be mutually exclusive and can be
synergistic (Widiger
& Samuel, 2005). Toward this aim
of developing a hybrid system, diagnostic-level IRM studies may
be useful for addressing the reorganization of existing
disorders along a common latent dimension in an empirically
governed way. IRM may also be facilitative in light of recent
suggestions to dimensionalize existing DSM
categories (First,
2005) or to provide supplemental dimensional
severity ratings (Brown
& Barlow, 2005) on the basis of
information conveyed by a specific pattern of diagnoses via the
use of the EAP or other latent trait scores. However, clinical
considerations (e.g., the reasons patients present for
treatment; Zimmerman
& Mattia, 2000) must also be
weighed, and these may not be fully captured by a single
severity score on a shared dimension.
Our study lends
support to the reorganization proposed by the hierarchical
quantitative model of Watson and colleagues (e.g.,
Watson &
Clark, 2006): Rather than their current
placement into two separate categories (i.e., mood disorders and
anxiety disorders), the five disorders examined here are better
grouped under one overarching category representing a general
factor (i.e., internalizing), which better describes their
covariation and for which they each act as higher-end
indicators. Supporting prior work (Brown et al., 1998;
Watson,
2005), our data also uphold a particularly
strong association between GAD and MDD that is ignored by the
mood and anxiety distinction in the current
DSM. Because twin studies have found that GAD
and MDD are genetically indistinguishable (Kendler, 1996) and
because antidepressant medications have shown efficacy in the
treatment of GAD (Gorman, 2002), a reorganization
explicitly acknowledging this by placing GAD and MDD within the
same class may more optimally advance research on etiological
processes and treatment than the current
system. Further, our finding that GAD evidenced
the greatest discrimination for internalizing is consistent with
the findings of Brown et al. (1998), who also found that GAD
consistently exhibited the largest correlations with other
DSM–IV mood and anxiety disorders
and evidenced the largest association with a general dimension
of negative affect.
The use of IRM
conceivably may exert an influence on sculpting future editions
of clinical interviews based on DSM
criteria—which are often time consuming and difficult
to administer—to be more efficient. For example, our
data suggest that these mood and anxiety disorders, currently
assessed in separate modules with an intervening psychotic
disorders module separating them, should instead be combined.
Our findings suggest that assessment for specific phobia should
be placed after assessment for the other four disorders in such
a combined module. In the current SCID, specific phobia
sequentially precedes GAD, a disorder both more likely to be
endorsed and more informative to the internalizing dimension.
Though providing
evidence that a shared underlying factor of internalizing
sufficiently accounts for the covariation among these disorders,
both unidimensional IRM studies fail to address further possible
subspecifications within this general factor (i.e., the
placement of the disorders among distress versus fear
subclasses), and the modest tetrachoric correlations and factor
loadings of this study suggest the diagnoses contain large
specific components in addition to the common factor they share.
Future work in which IRM is applied to an internalizing spectrum
should include additional diagnostic indicators to further
inform the internalizing dimension. We purposefully did not
include in our study PTSD, OCD, and bipolar disorder to maximize
comparability with the study by Krueger and Finger (2001), who
also did not include these. Our psychiatric sample may overcome
the low base rates of OCD and bipolar disorder that have
hampered their inclusion for structural analyses with general
populations. We would expect that at the level of diagnostic
analysis, both PTSD and OCD would show significant, positive
correlations with the five disorders of this study, and each
would exhibit large discrimination parameters towards
internalizing. Determining the status of bipolar disorder within
an internalizing spectrum at a diagnostic level, however, is
likely to be more problematic. DSM rules render
the diagnosis mutually exclusive with MDD, creating difficulty
for correlational modeling. Also, within-disorder heterogeneity
may be even more pronounced for bipolar disorder, because the
diagnostic label could conceptually encompass a group ranging
from hypomanic patients who report increased efficiency in
functioning (i.e., minimal to no internalizing) to severely
depressed patients needing hospitalization (i.e., high
internalizing). In a recent factor analysis by
Watson
(2005), bipolar disorder had weak and
virtually identical loadings on the three factors identified by
Krueger
(1999). Finally, a comprehensive structural
scheme should model syndromes currently grouped in other
diagnostic classes outside mood and anxiety (Watson, 2005), for
example, the externalizing disorders spectrum to which the
internalizing disorders are more moderately correlated
(Krueger,
1999).
The recent debates
over the current categorical DSM system reflect
a momentous juncture in our psychiatric nosology. Like
Krueger and Finger
(2001), we view this study as
preliminary research in a fertile new area. We hope that further
IRM research will elucidate the relationship between currently
designated mood and anxiety disorders and their place in a
broader diagnostic system. Diagnostic covariation presented in a
more consistent manner may better guide prevention and
treatment.
Footnotes 1 To maintain
consistency with Krueger
and Finger (2001), we retained the label
internalizing to describe the higher order
factor. As they noted, this label is likely closely aligned with
others denoting the shared variance between depression and anxiety,
including negative affect (Watson & Clark, 1984), neuroticism
(Eysenck,
1994), and negative emotionality
(Waller, Tellegen,
McDonald, & Lykken, 1996).
2 One exception in which
skipouts were not used in the MIDAS project was MDD, for which
participants were queried for all symptom criteria for research
purposes not related to the current study. This suspension of the
skipout rule had no impact on the DSM–IV
diagnosis assigned.
3 Item difficulty and
item discrimination are the traditional labels given to the 2PL
model parameters. Although we retained their use here, it should be
acknowledged that these labels reflect origins in test construction
for maximum performance assessment of cognitive ability. In its
original context, item difficulty referred to the proportion of
individuals able to answer a test item correctly (i.e., how
difficult to pass an item is) and may be somewhat of a misnomer when
applied to diagnostic endorsement data that are collected by a
different process: retrospective, self-referenced recall assessment.
Aggen, Neale, and Kendler
(2004) have argued for the term
liability threshold as a more appropriate
descriptor.
4 In addition to
difficulty and discrimination parameters, a third statistic reported
in Krueger and Finger
(2001), the root-mean-square posterior
residual (RMSPR), was not available as part of the MULTILOG package
and is not reported here.
5 We evaluated the
justification for using the 2PL model over the simpler one-parameter
IRT model in which discrimination slopes are held constant across
all indicators. The difference between the chi-square values for the
two models was 12.1 (df = 4, p
< .05) in favor of the 2PL, indicating that item
discrimination should be allowed to vary across diagnoses.
6 Note that in the
reanalysis of the data upholding the exclusion rule of the
DSM–IV (i.e., excluding those cases
of modified GAD from GAD), the tetrachoric correlation between GAD
and MDD attenuated from .31 to .15 and became the second lowest
association among the five disorders overall.
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Submitted: July 21, 2006 Revised: January 16, 2007 Accepted: January 25, 2007
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Source: Journal of Abnormal Psychology. Vol. 116. (3), Aug, 2007 pp. 464-474)
Accession Number: 2007-11737-003
Digital Object Identifier: 10.1037/0021-843X.116.3.464
Record: 19- Title:
- Exposure to rapid succession disasters: A study of residents at the epicenter of the Chilean Bío Bío earthquake.
- Authors:
- Garfin, Dana Rose. Department of Psychology and Social Behavior, University of California, Irvine, CA, US
Silver, Roxane Cohen. Department of Psychology and Social Behavior, University of California, Irvine, CA, US, rsilver@uci.edu
Ugalde, Francisco Javier. Instituto de Salud Pública, Universidad Andrés Bello, Santiago, Chile
Linn, Heiko. Instituto de Salud Pública, Universidad Andrés Bello, Santiago, Chile
Inostroza, Manuel. Instituto de Salud Pública, Universidad Andrés Bello, Santiago, Chile - Address:
- Silver, Roxane Cohen, Department of Psychology & Social Behavior, University of California, 4201 Social & Behavioral Sciences Gateway, Irvine, CA, US, 92697-7085, rsilver@uci.edu
- Source:
- Journal of Abnormal Psychology, Vol 123(3), Aug, 2014. pp. 545-556.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 12
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- Latin America, disasters, earthquakes, posttraumatic stress symptoms, trauma
- Abstract:
- We examined cumulative and specific types of trauma exposure as predictors of distress and impairment following a multifaceted community disaster. Approximately 3 months after the 8.8 magnitude earthquake, tsunami, and subsequent looting in Bío Bío, Chile, face-to-face interviews were conducted in 5 provinces closest to the epicenter. Participants (N = 1,000) were randomly selected using military topographic records and census data. Demographics, exposure to discrete components of the disaster (earthquake, tsunami, looting), and exposure to secondary stressors (property loss, injury, death) were evaluated as predictors of posttraumatic stress (PTS) symptoms, global distress, and functional impairment. Prevalence of probable posttraumatic stress disorder was 18.95%. In adjusted models examining specificity of exposure to discrete disaster components and secondary stressors, PTS symptoms and global distress were associated with earthquake intensity, tsunami exposure, and injury to self/close other. Increased functional impairment correlated with earthquake intensity and injury to self/close other. In adjusted models, cumulative exposure to secondary stressors correlated with PTS symptoms, global distress, and functional impairment; cumulative count of exposure to discrete disaster components did not. Exploratory analyses indicated that, beyond direct exposure, appraising the tsunami and looting as the worst components of the disaster correlated with greater media exposure and higher socioeconomic status, respectively. Overall, threat to life indicators correlated with worse outcomes. As failure of government tsunami warnings resulted in many deaths, findings suggest disasters compounded by human errors may be particularly distressing. We advance theory regarding cumulative and specific trauma exposure as predictors of postdisaster distress and provide information for enhancing targeted postdisaster interventions. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Distress; *Natural Disasters; *Symptoms; *Trauma
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Aged; Aged, 80 and over; Chile; Disasters; Earthquakes; Female; Humans; Life Change Events; Male; Middle Aged; Stress Disorders, Post-Traumatic; Surveys and Questionnaires; Young Adult
- PsycINFO Classification:
- Psychological Disorders (3210)
- Population:
- Human
Male
Female - Location:
- Chile
- Age Group:
- Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs)
Aged (65 yrs & older)
Very Old (85 yrs & older) - Tests & Measures:
- Short Form 36 Health Survey
Brief Symptom Inventory DOI: 10.1037/t00789-000
PTSD Checklist - Methodology:
- Empirical Study; Interview; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Jun 3, 2014; Revised: Jun 2, 2014; First Submitted: Aug 14, 2013
- Release Date:
- 20140804
- Copyright:
- American Psychological Association. 2014
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0037374
- PMID:
- 25089656
- Accession Number:
- 2014-31174-001
- Number of Citations in Source:
- 52
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-31174-001&site=ehost-live">Exposure to rapid succession disasters: A study of residents at the epicenter of the Chilean Bío Bío earthquake.</A>
- Database:
- PsycINFO
Exposure to Rapid Succession Disasters: A Study of Residents at the Epicenter of the Chilean Bío Bío Earthquake
By: Dana Rose Garfin
Department of Psychology and Social Behavior, University of California, Irvine
Roxane Cohen Silver
Department of Psychology and Social Behavior, Department of Medicine, and Program in Public Health, University of California, Irvine;
Francisco Javier Ugalde
Instituto de Salud Pública, Universidad Andrés Bello, Santiago
Heiko Linn
Instituto de Salud Pública, Universidad Andrés Bello, Santiago
Manuel Inostroza
Instituto de Salud Pública, Universidad Andrés Bello, Santiago
Acknowledgement: We thank Pedro Uribe Jackson, MD (Universidad Andrés Bello), for his support of the project; the staff at Ipsos for their expertise with sampling design, weighting data, and survey administration; and JoAnn Prause, PhD (University of California, Irvine), for her statistical expertise. Project funding provided by Universidad Andrés Bello School of Medicine, Santiago, which played no other role in the research project or this article.
Superstorm Sandy of 2012, the 2011 Tôhoku Japanese earthquake, and Hurricane Katrina illustrate that natural disasters rarely occur in isolation. Frequently, one catastrophe begets a sequence of deleterious natural and man-made events, exacerbated by interrelated, associated disasters such as levee breakage, looting, or failure of governments to provide significant warnings or timely aid. Globally, natural disasters are increasing in number and severity; recent estimates indicate a 4.4%–7.5% lifetime prevalence of disaster exposure (Kessler, McLaughlin, Koenen, Petukhova, & Hill, 2012). The sixth largest recorded earthquake, an 8.8 magnitude temblor, struck off the coast of Concepción in Bío Bío, Chile, on February 27th, 2010. Millions of people were affected, 521 died, 12,000 were injured, and over 800,000 were displaced (American Red Cross Multi-Disciplinary Team, 2011). The Chilean earthquake typifies many multifaceted modern natural disasters. The earthquake (a primary precipitating event) was followed by two rapid-succession–associated disasters: a devastating tsunami and subsequent flooding that, through failure of the Hydrographic and Oceanographic Service of the Chilean Navy (SHOA), occurred without adequate warning, and several days of looting in the epicenter region.
Exposure to natural disasters is frequently associated with postdisaster mental health problems such as posttraumatic stress disorder (PTSD), global distress, and functional impairment (for reviews, see Garfin & Silver, in press; Norris et al., 2002), although many survivors will exhibit striking resilience (Bonanno, Brewin, Kaniasty, & La Greca, 2010). Much prior literature has nonetheless been limited by methodological weaknesses (e.g., nonrepresentative samples) and a narrow inclusion of predictors and outcomes (Bonanno et al., 2010; Garfin & Silver, in press). The present study addressed these limitations through a theoretically derived, multivariate inquiry into predictors of postdisaster distress and functioning using an epidemiological sample of adults directly exposed to the Bío Bío earthquake. Within a cross-cultural setting, we advance theories regarding responses to disasters specifically and trauma more generally by examining the influence of type and amount of trauma exposure and other key predictors, such as predisaster individual characteristics (Brewin, Andrews, & Valentine, 2000; Ozer, Best, Lipsey, & Weiss, 2003), on several postdisaster outcomes following the earthquake and associated disasters. Each will be considered in turn.
Type of DisasterMultifaceted disasters are common, yet surprisingly few studies have unpacked potential differences in how the type of disaster correlates with negative outcomes. Although the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5, American Psychiatric Association, 2013) groups all potentially traumatic events under “Criterion A” (stressor) for PTSD, research on risk perception has indicated differential associations between disaster types and hazard judgments (e.g., Ho, Shaw, Lin, & Chiu, 2008); such variability may also extend to other outcomes including postdisaster psychopathology. Decades ago, Brim (1980) theorized that the type of life event might differentially influence psychological processes. Baum (1987) posited community disasters with a man-made component might elicit greater distress compared to other events. In contrast, events involving social breakdown, such as looting, might more strongly influence distress by violating a world view that assumes community safety and trustworthy neighbors (Janoff-Bulman, 1992).
Yet recent research has largely ignored specificity in disaster type. Exceptions include Norris and colleagues’ (2002) literature review, which suggested violent disasters may be correlated with worse outcomes, and an empirical study comparing victims of political violence and earthquakes that found no differences between groups experiencing one event compared to another (Goenjian et al., 1994). Exploring how discrete disaster components (earthquake, tsunami, looting) correlate with psychological outcomes may address these theoretical questions and inform targeted allocation of limited postdisaster resources.
Exposure to Disaster-Related Secondary StressorsExposure to individual-level stressors occurring during or in the immediate aftermath of a disaster (e.g., property loss, injury, death) may influence mental health outcomes. Such occurrences have been conceptualized as “secondary stressors” in past postdisaster epidemiological studies (Galea et al., 2007; Kessler et al., 2012). After Hurricane Katrina, an event conceptually similar to the Chilean earthquake (i.e., a multifaceted natural disaster exacerbated by man-made failings), specificity in exposure to individual-level secondary stressors was associated with DSM–IV (American Psychiatric Association, 2000) anxiety disorders and PTSD (Galea et al., 2007); physical injury and adversity were particularly strong correlates of distress for those highly exposed. Clarifying how specific disaster-related stressors may be associated with postdisaster outcomes could further refine the design of interventions and answer theoretical questions regarding the role of specificity of traumatic stress exposure in negative outcomes (Brewin et al., 2000; Galea et al., 2007; Ozer et al., 2003).
Cumulative ExposureAlternatively, cumulative—rather than a specific type of—exposure to discrete disaster components and specific secondary stressors may predict postdisaster difficulties (Norris et al., 2002; Seery, Holman, & Silver, 2010; Turner & Lloyd, 1995). For example, after the 1988 Armenian earthquake, combined earthquake and political violence exposure predicted psychological distress; differential responses were not found between groups exposed to only one of those two events (Goenjian et al., 1994). More generally, number of traumatic events often predicts negative outcomes (Chapman et al., 2004), although not necessarily in a linear, “dose-response” relationship (Seery et al., 2010). Furthermore, exposure to negative events often co-occurs, particularly after large-scale disasters, yet few studies have considered additive effects of exposure to greater numbers of discrete disaster components or the secondary stressors that accompany such catastrophes.
Predisaster Individual CharacteristicsEmpirical evidence also indicates that preexisting individual-level characteristics can influence postdisaster mental health (Hobfoll, 1989; Norris et al., 2002). Demographic and socioeconomic indicators are frequently implicated, albeit at times inconsistently (e.g., Brewin et al., 2000; Norris et al., 2002). For example, females (Bonanno et al., 2010), individuals from disadvantaged backgrounds (Norris et al., 2002), and those with prior mental health problems (Norris et al., 2002; Silver, Holman, McIntosh, Poulin, & Gil-Rivas, 2002) are typically at greater risk for difficulties postdisaster. The roles of marital status and age in postdisaster outcomes have been inconsistent (Brewin et al., 2000; Norris et al., 2002), although married persons and younger individuals tend to exhibit different responses than comparison groups; age effects may vary based on event-type and outcome measure (Garfin & Silver, in press; Scott, Poulin, & Silver, 2013). Consequently, such individual-level characteristics should be considered in epidemiological assessments of postdisaster mental health and functioning.
The Present StudyIn sum, prior work suggests that type of traumatic event (both disaster component and secondary stressor) may differentially influence postdisaster psychological outcomes. Other evidence indicates that the aggregate number of traumatic events may also be an important indicator of negative outcomes. Little, if any, research has contrasted these predictors following a disaster where a series of catastrophic events (earthquake, tsunami, looting) and a variety of secondary stressors occur rapidly. Moreover, as noted in seminal meta-analyses (Brewin et al., 2000; Ozer et al., 2003), a key problem with examining theoretical predictors of posttraumatic responses is the heterogeneity of precipitating traumas. Studying reactions to an exogenous sequence of events such as the Chilean earthquake—with clearly demarcated categories of exposure—allows for a naturalistic “control” of factors that typically vary when comparing across disasters (e.g., Kessler et al., 2012) or other traumatic events (e.g., comparing child abuse to military combat; Seery et al., 2010).
The present study examined how exposure to different types of disaster component (earthquake, tsunami and subsequent flooding, looting) and secondary stressors (property loss, injury, death) differentially predicted deleterious outcomes following the Bío Bío Chilean earthquake. Specificity of exposure was also compared to cumulative exposure to these stressors. In addition, the role of predisaster individual characteristics was considered. We had several predictions. First, we expected that both specificity in exposure to secondary stressors and associated disasters, as well as cumulative counts of exposure, would be associated with negative outcomes. Second, similar to past epidemiological studies, we expected individual-level predictors (female gender, lower socioeconomic status [SES], mental health history) to correlate with postdisaster responses. We also explored which disaster components would be appraised as the worst. The tsunami, which had a man-made component, might be most distressing, yet the looting might be viewed as worse since it represented a breakdown in perceptions of community safety and/or benevolence of one’s neighbors.
Method Procedures
Shortly after the earthquake, Ipsos Public Affairs, an international policy and market research company, obtained a representative sample of 2,008 Chilean adults aged 15–90 who lived in provinces across Chile; the present study utilized a subsample of Chileans who lived in five regions closest to the earthquake’s epicenter (Concepción, Talcahuano, Tomé, Lota, and Talca). Data were collected via 35–40 minute face-to-face interviews from May 13 to June 7, 2010. Demographic quota sampling cells, constructed from Chilean National Statistics Institute census population estimates of region, gender, and age, determined participation eligibility. Geographic sampling maps were derived from these estimates along with topographic data from the Military Geographic Institute. Interviewers approached 4,221 homes and contacted a total of 1,711 eligible individuals; 1,004 participated in the interviews, resulting in a 59% participation rate. Demographic information (age, marital status, gender) was recorded by interviewers.
Homes were approached at least twice at different times of the day to account for varying work/activity schedules. If residents could not be reached, the interviewer would solicit information from neighbors to ensure vacancies were not systematic (e.g., due to property loss during the disaster or socioeconomic status). Interviewers attempted to find absent residents based on neighbor reports of work schedule, vacation plans, or relocation of the household to another property. Since the majority of people who lost their homes from the earthquake subsequently resided in tents on their own property (Jaime Vásquez, personal communication, 2013), earthquake-related vacancies were not a serious concern in interview solicitation.
Interviews were conducted in Spanish by professional staff trained by Ipsos in administering face-to-face interviews. Verbal consent was obtained from all participants. All measures were written in English and then translated and back-translated by Chilean bilingual psychologists (FJU, HL) and checked for linguistic and cultural accuracy.
Data from the interviews were entered manually into a database, with 5% of all responses reentered to check for data entry errors. The study was approved by the Institutional Review Boards at the University of California, Irvine, and Universidad Andrés Bello, Santiago.
Outcome Measures
Posttraumatic stress (PTS) symptoms
The PTSD Checklist (PCL; Weathers, Litz, Herman, Huska, & Keane, 1993), a well-validated 17-item self-report measure, was used to assess PTS symptoms. Individuals rated how distressed or bothered they were by symptoms related to the Chilean earthquake, tsunami, and their aftermath over the prior 7 days, with endpoints 1 (not at all) to 5 (extremely). Responses were summed to create a continuous measure of PTS symptoms (range 17–85); this continuous measure was utilized to account for variability in symptom severity in an inherently dimensional construct (cf. MacCallum, Zhang, Preacher, & Rucker, 2002). To estimate prevalence of probable PTSD, the PCL was scored according to a cutoff of 50, which is the most conservative estimate commonly used, as well as using DSM–IV scoring criteria (Ruggiero, Del Ben, Scotti, & Rabalais, 2003). Studies using confirmatory factor analysis have shown equivalence between Spanish and English versions of the PCL (Marshall, 2004).
Global distress
Distress was measured using the 18-item Brief Symptom Inventory (BSI-18; Derogatis, 2001). Respondents indicated their level of distress in the past 7 days (including the day of completion), with endpoints 1 (not at all) to 5 (extremely). The BSI-18 has been validated in community-based and medical samples and has demonstrated excellent reliability in field studies (Derogatis, 2001). Spanish versions have shown equivalence (Ruipérez, Ibáñez, Lorente, Moro, & Ortet, 2001). Internal consistency was excellent (range 18–90; α = .95).
Functional impairment
Four items modified from the Short Form 36 Health Survey (SF-36; Ware & Sherbourne, 1992) assessed impairment in work and social activities occurring as a consequence of physical or emotional health problems (range = 1–5, α = .93). A similar modification of the SF-36 was used in a prior epidemiological assessment of psychological outcomes following exposure to adverse events (Seery et al., 2010); Spanish versions of the SF-36 have indicated equivalence (Alonso, Prieto, & Antó, 1995).
Disaster-Related Characteristics
Earthquake intensity
The degree of destruction experienced during the earthquake was assessed using a version of the Modified Mercalli Intensity Scale (Wood & Neumann, 1931), commonly implemented to assess earthquake intensity for the nonscientist population (U.S. Geological Survey, 2013a). Participants reported their experience of the earthquake the night it occurred on an 8-point scale: 1 (not perceptible), 2 (felt slightly, no damage to objects), 3 (weakly felt, objects moved slightly), 4 (objects swayed, glass and windows rattled), 5 (strong shaking or rocking of entire building), 6 (objects broke, cracks in plaster), 7 (serious damage to surroundings), 8 (destructive, forcibly thrown to the ground, many objects broken, walls collapsed, location uninhabitable/unlivable). Four participants indicated that the earthquake was “not perceptible”; they were deleted from the final sample, resulting in N = 1,000.
Two additional measures of earthquake intensity were computed: residential region and kilometers from the geologic epicenter. The pattern of results was identical for all three measures; results using the Mercalli Intensity Scale are reported in the text and tables as this measure accounted for geographic variability in earthquake destruction and intensity and is more commonly used in research on earthquakes.
Additional disaster exposure
Participants also reported whether they were at the coast as the tsunami occurred, coded 0 (not at the coast), 1 (at the coast when tsunami hit). Looting exposure was assessed by asking participants whether they witnessed looting directly, participated in looting, lost property in looting, or knew someone close who lost property in the looting; endorsing any of these exposures was considered an affirmative exposure, coded 0 (no looting exposure), 1 (looting exposure).
A continuous variable of cumulative disaster exposure was also created via a count of exposure to the three disasters (earthquake, tsunami, looting; M = 1.57, SD = 0.55, range 1–3).
Exposure to secondary stressors
Participants reported experience with three possible secondary stressors to which they or a close other could have been exposed as a result of the earthquake and its aftermath. To remain consistent with DSM–IV (American Psychiatric Association, 2000) criterion A for exposure to potentially traumatic events, both experiences for self and close other were assessed. Items were modified from prior research on community disasters (Holman & Silver, 1998; Silver et al., 2002). Disaster-related property loss was assessed and categorized 0 (no property loss) or 1 (personally lost property in earthquake, tsunami, or looting and/or close other lost property). Participants reported experience with injury resulting from the earthquake, tsunami, or looting; responses were categorized 0 (no injury) or 1 (personally injured and/or close other injured). Disaster-related death was also assessed; responses were dichotomized 0 (no death experience) or 1 (personally knew someone who died in the earthquake or tsunami).
Exposures to potential secondary stressors (personally lost property, close other lost property, injury to self, injury to close other, knew someone who died) were counted and combined into a continuous measure of cumulative secondary stressors experienced (M = 1.25, SD = 1.01, range = 0–5).
Disaster appraisal
Participants were asked which of the three components of the disaster (earthquake, tsunami and associated flooding, or looting) they experienced as the worst; participants could select only one.
Individual-Level Characteristics
Socioeconomic status (SES)
A socioeconomic score (called the E&E Socioeconomic Classification in Chile) was calculated using type of employment and education level of head of household. This measure is commonly used in Chilean market and epidemiological research and correlates strongly with household income (Asociación Investigadores de Mercado [AIM] Chile, 2008; Ipsos, 2010). The E&E is computed by asking respondents the education level (seven possible choices range from “less than primary school” to “graduate degree obtained”) and type of work (six possible choices range from “occasional work/unemployed” to “organization director”) of the head of household. Households are then categorized via a matrix of possible responses and grouped into the greater than 90th, 70th, 45th, 10th, and lower than 10th percentiles (AIM Chile, 2008; Ipsos, 2010); lower percentiles indicate higher SES. This score was standardized and used as a continuous measure of SES in analyses (M = 3.27, SD = 1.00, range 1–5).
Physician-diagnosed mental health history
Participants reported any doctor or health care professional diagnosis of depression or anxiety disorder prior to February 2010 (before the earthquake). A continuous variable of physician-diagnosed mental health ailments was coded 0 (no history of depression or anxiety disorder), 1 (history of depression or anxiety disorder), or 2 (history of both depression and anxiety disorder). Similar categorizations have been used in past research (e.g., Holman, Garfin, & Silver, 2014; Silver et al., 2002).
Demographics
Gender was coded 0 (male), 1 (female). Marital status was coded as (a) single (never married), (b) married, or (c) widowed, divorced, or separated. Married persons comprised the reference group (coded “0” in analyses) as they often exhibit differential outcomes when compared to individuals who do not have a spouse present (Garfin & Silver, in press). Age was grouped into six categories (15–24, 25–34, 35–44, 45–54, 55–64, 65+). Individuals 15–24 years old comprised the reference group (coded “0” in analyses) since past research suggests younger individuals exhibit differential postdisaster distress responses when compared to older individuals (Garfin & Silver, in press; Norris et al., 2002).
Statistical Analyses
Statistical analyses were conducted using STATA 11.0 (Stata Corp, College Station, TX), a program well-suited for handling weighted survey data. Ipsos provided poststratification weights, calculated by multiplying individuals in a given demographic category (i.e., age, city population, gender) by a factor proportional to Census estimates of that particular demographic category and inversely proportional to the number obtained in our sample. Analyses were then weighted to adjust for differences in sample composition compared to Chilean census data, facilitating stronger population-based inferences.
First, we calculated descriptive statistics of exposure to the earthquake, tsunami and looting, PTS symptoms and probable rates of PTSD, and participants’ appraisal of which disaster component was the worst. Then, bivariate regression analyses examined independent associations between each of the three outcome variables (PTS symptoms, global distress, functional impairment) and individual and cumulative exposure to the disasters (earthquake, tsunami, looting) and individual and cumulative exposure to secondary stressors (property loss, injury, death).
Multivariate methods are recommended for postdisaster epidemiological studies to illustrate the independent contribution of covariates while controlling for the relative contribution of predictors (Bonanno et al., 2010). We conducted multivariate regression models that analyzed predictors of PTS symptoms, global distress, and functional impairment. For each of the three outcome variables, two sets of multivariate ordinary least squares (OLS) regression models were constructed using a hierarchical variable entry strategy. The first set examined the potential specific effects of exposure to the disasters and their secondary stressors. The second set examined the potential cumulative effects of exposure to the disasters (earthquake, tsunami, looting) and three types of secondary stressors (property loss, injury, death). On Step 1, disaster exposure variables (either dummy coded exposure variables to examine specific effects or counts of exposure to examine cumulative exposure) were entered. On Step 2, all other variables (physician-diagnosed mental health history, SES, demographics) were entered.
Interactions between specific and cumulative exposure to the three components of the disaster and specific and cumulative exposure to secondary stressors were examined. Interactions between SES and the three secondary stressors were tested according to theoretical significance (Galea et al., 2007).
Results Sample
Table 1 presents the demographic composition of the sample compared to Chilean census benchmarks. The sample was 46.10% married (unweighted n = 456); 12.74% widowed, divorced, or separated (unweighted n = 128); and 41.16% single (unweighted n = 415).
Demographic Composition of the Sample and Comparisons With Chilean Census Dataa (N = 1,000)
Table 2 presents weighted and unweighted percentages of participants’ exposure to the Chilean disaster, the component of the disaster participants appraised as the worst, the percentage with PTS symptoms, and rates of probable PTSD. Almost half of the sample reported intrusion/reexperiencing symptoms, and depending on scoring criteria, almost one fifth met DSM–IV diagnostic criteria for probable PTSD. Mean score on the PCL = 30.11 (95% confidence interval [CI], 29.18–31.05), M on the BSI-18 = 31.31 (95% CI, 30.39–32.22), and on the measure of functional impairment, M = 1.55 (95% CI, 1.49–1.60). All participants were directly exposed to the earthquake; approximately 46% of the sample (unweighted n = 454) did not have direct experience with an associated disaster (tsunami or looting). Direct exposure to the looting was reported by 49.56% (unweighted n = 498), 26 (2.63%) participants were exposed to the tsunami but not the looting, and 22 (2.08%) were directly exposed to all three disasters.
Disaster Exposure and Posttraumatic Stress Symptomatology (N = 1,000)
Correlates of Psychological Outcomes
Table 3 presents bivariate relationships between specific and cumulative disaster exposure variables and PTS symptoms, global distress, and functional impairment (not adjusting for covariates) to illustrate the independent effects of predictors included in the multivariate models. Earthquake intensity was positively associated with PTS symptoms, global distress, and functional impairment; exposure to the tsunami was associated with PTS symptoms and global distress; and exposure to the looting was negatively associated with functional impairment. Property loss (to self or close other) and injury (to self or close other) were positively associated with PTS symptoms, global distress, and functional impairment. Knowing someone who died in the earthquake or tsunami was not associated with any of the three outcome variables. Cumulative exposure to disasters and cumulative number of secondary stressors (property loss, injury, death) were positively associated with PTS symptoms, global distress, and functional impairment.
Bivariate Relationships Between Exposure Variables and Posttraumatic Stress Symptoms, Global Distress, and Functional Impairmenta
Specific Exposure to Disasters and Secondary Stressors
Table 4 presents standardized OLS regression coefficients for type of exposure to the disasters and secondary stressors, other key predictor variables, and PTS symptoms, global distress, and functional impairment. As depicted under Step 1 for each outcome variable, after controlling for the relative contribution of each exposure variable listed, earthquake intensity was positively associated with PTS symptoms, global distress, and functional impairment. Tsunami exposure was positively associated with PTS symptoms and global distress. Exposure to looting was negatively associated with functional impairment. Property loss and injury were associated with PTS symptoms, global distress, and functional impairment. The columns under Step 2 illustrate the correlation between exposure variables and each of the three outcome variables after controlling for the relative contribution of the other predictor variables. These results illustrate that earthquake intensity, injury, physician-diagnosed mental health history, lower SES, and female gender were positively associated with PTS symptoms, global distress, and functional impairment.
Multivariate Ordinary Least Squares Regression Analyses of Key Predictor Variables, Exposure to Specific Disaster-Related Events, and Posttraumatic Stress Symptoms (N = 974),a Global Distress (N = 968),a and Functional Impairment (N = 985)a
Cumulative Exposure to Disasters and Secondary Stressors
Table 5 presents standardized OLS regression coefficients for cumulative exposure to disaster-related events, other key predictor variables, and PTS symptoms, global distress, and functional impairment. While cumulative disaster exposure was not associated with any of the three outcomes in any of the multivariate analyses, cumulative secondary stressor exposure was associated with all three outcomes. In addition, earthquake intensity was positively associated with PTS symptoms, global distress, and functional impairment. Female gender, physician-diagnosed mental health history, and lower SES were correlated with PTS symptoms, global distress, and functional impairment (see Step 2 under each outcome variable).
Multivariate Ordinary Least Squares Regression Analyses of Key Predictor Variables, Cumulative Exposure to Disaster-Related Events, and Posttraumatic Stress Symptoms (N = 974),a Global Distress (N = 968),a and Functional Impairment (N = 985)a
Interactions
None of the interaction terms examined were significant predictors of any of the three outcomes.
Exploratory Analyses
Interestingly, the number of participants (n = 250, 24.74%) who endorsed the tsunami as the worst component of the disaster was substantially greater than the number who reported experiencing the disaster as it occurred (n = 48, 4.71%). We conducted post hoc analyses to examine what factors, including direct exposure to the event, might be associated with selection of the worst component. A multivariate multinomial logistic regression identified predictors of one’s appraisal of the worst aspect of the disaster; selecting the earthquake comprised the base category (i.e., served as the comparison group). Earthquake intensity, associated disaster exposure (tsunami and/or looting), secondary stressors (property loss, injury, death), SES, and postdisaster media exposure, were selected as potential correlates, consistent with recent epidemiological research on collective trauma (Holman et al., 2014). To assess media exposure, participants reported, on average, how many hours per day they spent (a) watching TV or listening to radio coverage of the earthquake, tsunami, and their aftermath; and (b) reading books, magazines or newspaper coverage of the earthquake, tsunami, and their aftermath. Responses were averaged (M = 1.77, SD = 2.52, range = 0–12.5) to obtain a mean media exposure score.
Results are reported as relative rate ratios (RRRs), which can be interpreted in a manner similar to odds ratios in logistic regression analyses. Endorsing the tsunami as the worst aspect of the disaster was positively associated with having been at the coast where the tsunami hit (RRR = 3.19, 95% CI, 1.61–6.32, p = .001) and with increased disaster-related media exposure (RRR = 1.04, 95% CI, 0.97–1.11, p = .003); it was negatively associated with directly experiencing the looting (RRR = 0.61, 95% CI, 0.44–0.84, p = .003). Endorsing the looting as the worst component of the disaster was associated with higher SES (RRR = 0.62, 95% CI, 0.53–0.72, p < .001).
DiscussionThe Bío Bío earthquake resulted in a series of traumatic events and mental health consequences for many residents near the epicenter. Logistical difficulties such as obtaining funding and rapid ethics approval typically preclude short-term postdisaster psychiatric epidemiological assessments (Norris, 2006). Nonetheless, early postdisaster assessments may help inform intervention efforts (Bryant & Litz, 2009) by helping to identify at-risk populations, important given the potential benefit of short-term interventions (Bonanno et al., 2010). By collecting data among a demographically representative sample of directly exposed residents shortly after the earthquake, we improve on methodological limitations of prior research and address the charge to use more sophisticated techniques in postdisaster assessments (Bonanno et al., 2010; Kessler et al., 2012). Moreover, our sample closely matched Chilean census benchmarks, strengthening population-based inferences and increasing the generalizability of our findings.
Rapid succession disaster sequences are common yet underexplored in the extant literature (Kessler et al., 2012); the present study addressed this absence and explored the relationship between rapid succession disaster exposure and subsequent responses. We found that specific, but not cumulative, exposure to the earthquake and associated disasters (tsunami, looting) was correlated with negative outcomes. Second, cumulative counts of and specificity in exposure to secondary stressors were both associated with adverse psychological outcomes. Lastly, several demographic predictors elucidated variability in postdisaster responses.
Type of Trauma Exposure
Results advance our understanding of differential effects of exposure to different types of traumatic events. Contrasting results from Tables 4 and 5 highlight the importance that disaster type has on distress responses. Distress was more strongly associated with the specific type (i.e., the tsunami; see Table 4)—rather than with the number (see Table 5)—of disaster components experienced. Although the prevalence of PTSD after natural disasters is typically lower than that occurring after man-made or technological disasters (Norris et al., 2002), prior research has not explored natural disasters compounded by human errors. Results indicated that exposure to the destructive tsunami, occurring despite assurances from the government that the coastal area was safe, had an independent contribution to deleterious outcomes. Negative psychological outcomes have been observed following traumatic events that were another person’s fault (Delahanty et al., 1997); disasters (such as the tsunami) that stem from or are exacerbated by large-scale failures of trusted authorities may be detrimental by a similar process. Our findings thus support theoretical models positing disasters caused or worsened by human failings may elicit greater distress (Baum, 1987). Interestingly, exposure to the looting was not correlated with increased PTS or global distress and was negatively correlated with functional impairment (see Table 4). Perhaps for those who either personally participated in the looting or who knew a friend or family member who did so, the looting instilled a sense of control in an otherwise uncontrollable situation; greater sense of control has been linked with more adaptive functioning (Folkman, 1984).
Results indicate that exposure to specific types of individual-level secondary stressors independently predicts distress (see Table 4). More specifically, experiencing injury after the Bío Bío disaster was more strongly associated with negative outcomes than was experiencing property loss or knowing someone who died. In a related vein, the majority of the disaster-related deaths were caused by the tsunami. Taken together, these findings bolster theories postulating that threat to life, perhaps even more so than loss, drives the emergence of PTS symptoms (Momartin, Silove, Manicavasagar, & Steel, 2004). The looting was also human-perpetrated, but it could not be blamed on a single organization, and the participation of many community members in the looting may have weakened the link between exposure to the looting and negative outcomes.
Cumulative Exposure to Traumatic Events
As illustrated in Table 5, the cumulative number of disaster exposures (earthquake, tsunami, looting) was not associated with negative outcomes. However, cumulative exposure to (i.e., experiencing greater numbers of) individual-level secondary stressors (property loss, injury, and death) was significantly associated with PTS, global distress and functional impairment. The latter finding supports a growing body of research demonstrating that increased exposure to negative life events tends to correlate with subsequent adverse physical and mental health outcomes (e.g., Chapman et al., 2004; Felitti et al., 1998). Postdisaster screenings, clinical intakes, and research endeavors should assess both type and amount of trauma exposure to help identify survivors who might be most at risk for problems.
Individual-Level Characteristics
Several person-level characteristics were linked with negative outcomes. Females were more at risk for psychological problems, as expected (Norris et al., 2002; van Griensven et al., 2006). In contrast to previous findings (Norris et al., 2002), however, middle age and older adults were more susceptible to negative outcomes following the earthquake and its aftermath, highlighting the benefit of nuanced conceptualizations of age effects that consider type of outcome measure (Scott et al., 2013). Lower SES was strongly related to negative outcomes, bolstering growing research linking SES and postdisaster mental health (Garfin & Silver, in press) and identifying an additional population segment to target for interventions. Findings were also consistent with substantial literature linking past mental health problems with postdisaster maladies (Garfin & Silver, in press). Outreach with this population may be particularly important: people with a history of poor mental health are at greater risk for postdisaster distress, yet are also more likely to stop psychological treatments, exacerbating existing problems (Wang et al., 2008).
Cultural Concerns
Short-term epidemiological postdisaster mental health assessments of representative samples, especially those in non-Western nations, are limited. South America’s Pacific Coast is particularly vulnerable to devastating earthquakes; six of the 12 strongest earthquakes have occurred in this region, with Chile experiencing some of the strongest (U.S. Geological Survey, 2013b). Yet few postdisaster studies are conducted in Pacific Latin America; to our knowledge, no prior studies have used epidemiological data to examine reactions to earthquakes there. Possible cross-cultural differences in response to traumatic events highlight the value of conducting international research to understand region-specific reactions, as North American and European models of trauma assessments and interventions do not necessarily translate directly to all cultures (Draguns & Tanaka-Matsumi, 2003). Indeed, rates of psychiatric disorders vary greatly among epidemiological studies in Latin America; for example, Chileans reported lower rates of both trauma exposure and PTSD compared to Mexicans (Zlotnick et al., 2006). Whereas reexperiencing and arousal symptoms of PTS appear to be biologically derived and thus universally experienced, even within the United States, Latinos tend to report more avoidance symptoms, perhaps due to cultural mores promoting individual subordination to group well-being (Zayfert, 2008). This emphasizes the need for culturally specific prevalence rates of postdisaster psychopathology, which are important for estimating postdisaster service needs in a community.
Our results inform the historical record in this highly seismically active region of Latin America by documenting prevalence rates and examining predictors of psychological distress. Findings suggest that factors that tend to correlate with distress in European contexts (e.g., demographics, prior mental health, threat to life) translate to Latin American contexts. Future research should seek to replicate and expand these results in Latin American and other cultures (e.g., Asian, African) to generate a basis for stronger culturally specific clinical outreach and public policy recommendations.
Appraisals in the Postdisaster Context
Although 5% of participants were at the coast when the tsunami hit, almost 25% reported the tsunami and its subsequent flooding as the worst component of the disaster. The tsunami was associated with the greatest number of deaths and the resulting flood water took several weeks to subside, resulting in severe—and long-lasting—structural damage to the community. Other than having been physically present at the coast, the strongest correlate of endorsing the tsunami as the worst component of the disaster was event-related media exposure, highlighting the importance of media exposure as a predictor of postdisaster distress and the importance of the appraisal process following traumatic events (Janoff-Bulman, 1992). Moreover, results support emerging theories and empirical evidence that starkly contrast traditional views of trauma exposure, suggesting that trauma can be experienced vicariously; media exposure, for example, can be a more powerful predictor of stress responses to collective traumas than direct exposure (Holman et al., 2014).
Over a quarter of participants endorsed looting as the worst component of the disaster, which was associated with higher SES. Past research suggests community members from more economically and socially disadvantaged groups are more likely to participate in crimes following natural disasters (Zaharan, Shelley, Peek, & Brody, 2009). Given this, perhaps wealthier participants (and their friends and family members) refrained from engaging in looting activities. Furthermore, the looting may have challenged participants’ former belief in the benevolence or trustworthiness of other community members, a particularly important world view for some (Janoff-Bulman, 1992).
Limitations
Several limitations must be acknowledged. While data were collected in a shorter timeframe than most postdisaster epidemiological research, no assessments occurred within the first month after the earthquake, precluding inferences regarding acute stress reactions. We were also unable to explore change over time. Although we collected data on a sample that was representative of the population from which it was drawn, a portion of those eligible refused the interviews. Nonetheless, our response rate was substantially higher than the 20% typical in face-to-face survey assessments in South America (Jaime Vásquez, personal communication, 2013) and rigorous surveying techniques helped ensure that nonresponse was not primarily a function of degree of exposure to the disaster or demographic characteristics. While the PCL has been validated for use in Spanish, it has not been previously used in epidemiological studies in Chile specifically. Given the link between disaster exposure, reaction to stressors, and physical health problems (Holman et al., 2008), future research should also include objective measures of physical health outcomes. Lastly, because all of our participants were highly exposed to the earthquake, we did not have a no- or low-exposure comparison group, which may have shown disparate patterns of responses (Palinkas, Downs, Petterson, & Russell, 1993).
ConclusionsFindings advance theoretical understandings of postdisaster traumatic stress responses by indicating that specificity in type—rather than only the amount—of trauma exposure predicts variability in distress responses. Assessments that incorporate specific exposures that occur in the context of a larger disaster may improve research, policy, and clinical interventions following community catastrophes. Models that consider cumulative effects of trauma provide gross estimates of how increased trauma exposure may correlate with increased susceptibility to psychiatric maladies (Asarnow et al., 1999). Yet our findings suggest that a more fine-grained approach that considers the type of trauma exposure should also be considered, particularly after natural disasters, where it might be advantageous—and feasible—to identify people based on exposure to different events. Policies could target specific neighborhoods or communities with increased psychosocial services according to the component of the disaster sequence most heavily experienced. For example, communities more heavily impacted by disasters with a man-made component or with greater death tolls could be targeted more aggressively with short-term outreach efforts such as Psychological First Aid (Ruzek et al., 2007), and psychiatric screenings could include questions regarding specificity of disaster exposure.
Future research should continue to explore questions relating to both amount and nature of exposures following negative events, as well as the mechanisms (e.g., subjective interpretations, physiological reactions) behind these responses. Mixed methods that incorporate qualitative interviews may be especially useful in future studies. For example, qualitative interviews that ask participants to report why they felt one component of the disaster was worse than another may help elucidate psychological processes.
Methodologically, our study provides a model for successfully executing population-based short-term psychological assessments in an international context. Important for traumatic stress theory, results illustrate that postdisaster distress is not merely a function of cumulative exposure to traumatic events and secondary stressors, but is likely to be event- and experience-specific. More broadly, findings indicate appraisal of disaster severity may be influenced by factors such as media exposure and individual-level characteristics such as SES. Finally, targeting population segments based on demographic considerations, disaster experiences, and secondary stressors exposure may facilitate effective distribution of postdisaster services with the hope of informing humanitarian outreach efforts following multifaceted, rapid succession community disasters.
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Submitted: August 14, 2013 Revised: June 2, 2014 Accepted: June 3, 2014
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Source: Journal of Abnormal Psychology. Vol. 123. (3), Aug, 2014 pp. 545-556)
Accession Number: 2014-31174-001
Digital Object Identifier: 10.1037/a0037374
Record: 20- Title:
- Facets of anger, childhood sexual victimization, and gender as predictors of suicide attempts by psychiatric patients after hospital discharge.
- Authors:
- Sadeh, Naomi, ORCID 0000-0002-8101-3190. Department of Psychiatry, University of California, San Francisco, San Francisco, CA, US, naomisadeh@gmail.com
McNiel, Dale E.. Department of Psychiatry, University of California, San Francisco, San Francisco, CA, US - Address:
- Sadeh, Naomi, Department of Psychiatry, University of California, San Francisco, Box 0984-CPT, 401 Parnassus Avenue, San Francisco, CA, US, 94143, naomisadeh@gmail.com
- Source:
- Journal of Abnormal Psychology, Vol 122(3), Aug, 2013. pp. 879-890.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 12
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- anger, gender, sexual victimization, suicide attempts, psychiatric patients, hospital discharge, risk factors, at-risk, abuse history
- Abstract:
- Models of suicidal behavior that assess the interplay of multiple risk factors are needed to better identify at-risk individuals during periods of elevated risk, including following psychiatric hospitalization. This study investigated contributions of facets of anger, gender, and sexual victimization to risk for suicide attempts after hospital discharge. Psychiatric patients (N = 748; ages 18–40; 44% female) recruited from 3 inpatient facilities were assessed during hospitalization and every 10 weeks during the year following discharge as part of the MacArthur Violence Risk Assessment Study. Multiple logistic regression models with facets of anger (disposition toward physiological arousal, hostile cognitions, and angry behavior) from the Novaco Anger Scale (Novaco, 1994), gender, and childhood sexual victimization history were used to predict suicide attempts in the year following hospital discharge. Facets of anger differentially predicted suicide attempts as a function of gender and sexual victimization history, over and above the variance accounted for by symptoms of depression, anxiety, and recent suicide attempts. In men, greater disposition toward angry behavior predicted an overall greater likelihood of a suicide attempt in the year following hospital discharge, particularly among men with childhood sexual victimization. In women with a history of childhood sexual victimization, physiological arousal predicted suicide attempts. Results indicate that facets of anger are relevant predictors of suicide attempts following hospital discharge for psychiatric patients with a history of childhood sexual victimization. Further, results suggest that incorporating gender and victimization history into models of risk for suicide can help clarify relationships between anger and self-directed violence. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Attempted Suicide; *Hospital Discharge; *Psychiatric Patients; *Risk Factors; Anger; At Risk Populations; Child Abuse; Human Sex Differences; Sexual Abuse; Victimization
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Anger; Anxiety; Child Abuse, Sexual; Depression; Female; Hospitalization; Humans; Logistic Models; Longitudinal Studies; Male; Risk Factors; Sex Factors; Suicide, Attempted; Young Adult
- PsycINFO Classification:
- Psychological Disorders (3210)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs) - Tests & Measures:
- Diagnostic and Statistical Manual of Mental Disorder-3rd Edition-Revised Checklist
Structured Interview for Diagnostic and Statistical Manual of Mental Disorder-3rd Edition-Revised
Brief Psychiatric Rating Scale DOI: 10.1037/t01554-000
Novaco Anger Scale DOI: 10.1037/t02391-000 - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Jul 8, 2013; Accepted: Mar 22, 2013; Revised: Mar 22, 2013; First Submitted: Aug 12, 2012
- Release Date:
- 20130708
- Correction Date:
- 20130909
- Copyright:
- American Psychological Association. 2013
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0032769
- PMID:
- 23834063
- Accession Number:
- 2013-24297-001
- Number of Citations in Source:
- 86
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-24297-001&site=ehost-live">Facets of anger, childhood sexual victimization, and gender as predictors of suicide attempts by psychiatric patients after hospital discharge.</A>
- Database:
- PsycINFO
Facets of Anger, Childhood Sexual Victimization, and Gender as Predictors of Suicide Attempts by Psychiatric Patients After Hospital Discharge
By: Naomi Sadeh
Department of Psychiatry, University of California, San Francisco;
Dale E. McNiel
Department of Psychiatry, University of California, San Francisco
Acknowledgement:
Epidemiological surveys indicate that the prevalence of suicide is increasing in the United States (Crosby et al., 2011) and globally (World Health Organization, 2002, 2008). Approximately 1 million adults in the United States report attempting suicide within the last year (Crosby et al., 2011) and over half a million report visiting hospital emergency departments for suicide attempts and suicidal behavior (Centers for Disease Control, 2008). Psychiatric patients are at particularly elevated risk for suicide deaths and suicide-related behavior (e.g., Black, Winokur, & Nasrallah, 1987), and this risk increases following discharge from inpatient treatment (Bongar, 2002; Goldacre, Seagroatt, & Hawton, 1993). For instance, a national survey found that 24% (n = 519) of suicide deaths occurred within 3 months of discharge from a psychiatric hospital (Appleby et al., 1999). Similarly, a prospective study found that 3.3% of discharged inpatients had a suicide death, and over a third engaged in nonsuicidal self-injury or a suicide attempt within 6 months of hospital discharge (Links et al., 2012). These data highlight the need for additional research on factors that increase risk for suicide-related behavior following psychiatric inpatient treatment.
Suicide-related behavior is characterized by a range of thoughts and behaviors that increase the risk of suicide and vary in terms of lethal intent and physical injury (Silverman, Berman, Sanddal, O’Carroll, & Joiner, 2007). Examples include self-injury without the intent to kill oneself, thoughts of suicide, suicidal threats, and attempts to inflict lethal self-injury (G. K. Brown, Beck, Steer, & Grisham, 2000; Nock, Joiner, Gordon, Lloyd-Richardson, & Prinstein, 2006; Silverman et al., 2007). Research indicates that both nonsuicidal (without lethal intent) and suicidal (with lethal intent) ideation and behavior are important indicators for assessing risk for suicide (Fawcett et al., 1997).
Anger is potentially important to consider in models of suicide risk, because it is a symptom that cuts across multiple psychiatric diagnoses shown to be associated with elevated risk for suicide attempts, including posttraumatic stress disorder, borderline personality disorder, and antisocial personality disorder (M. Z. Brown, Comtois, & Linehan, 2002; Krysinska & Lester, 2010; Lieb, Zanarini, Schmahl, Linehan, & Bohus, 2004; Verona, Patrick, & Joiner, 2001; Wilcox, Storr, & Breslau, 2009). Thus, it has the potential to serve as a transdiagnostic indicator of risk for suicidal behavior across disorders with overlapping symptomatology (e.g., Nolen-Hoeksema & Watkins, 2011). Despite the potential relevance of anger as a risk factor for suicidal behavior, research to date has been relatively limited and produced mixed findings. On the one hand, there is a relatively large body of research that indicates anger and aggression are positively related to suicide attempts (Daniel, Goldston, Erkanli, Franklin, & Mayfield, 2009; Esposito, Spirito, Boergers, & Donaldson, 2003; Giegling et al., 2009; Swahn, Lubell, & Simon, 2004; Swogger, You, Cashman-Brown, & Conner, 2011) and suicidal behavior defined more broadly (e.g., nonsuicidal self-injury and suicide attempts; Gormley & McNiel, 2010; Horesh et al., 1997; Horesh, Gothelf, Ofek, Weizman, & Apter, 1999; Swogger, Walsh, Homaifar, Caine, & Conner, 2012). Yet, several studies have not found a reliable relationship between anger and suicidal behavior (Goldston et al., 1999; Horesh, Orbach, Gothelf, Efrati, & Apter, 2003; Kerr et al., 2007; Kingsbury, Hawton, Steinhardt, & James, 1999). Thus, more research is needed to delineate whether and under what circumstances anger confers risk for suicidal behavior. Accordingly, the present study examined whether (a) conceptualizing anger as a multidimensional construct and (b) including theoretically relevant moderators, specifically childhood sexual victimization and gender, into models of risk could further clarify anger relationships with suicide attempts.
Conceptualizing anger as a multidimensional rather than unitary construct may add specificity and clarity to models of anger as a risk factor for suicide-related behavior. Research suggests that anger can be reliably measured as distinct facets that each serve to activate and maintain anger, specifically dispositions toward physiological arousal, angry behavior, and hostile cognitions (Novaco, 1994). As described by Novaco (1994), the arousal facet characterizes the physiological activation and readiness for action component of anger. In contrast, the angry behavior facet describes behavioral manifestations of anger, and the hostile cognitions and attitudes facet indexes thought processes that initiate and maintain anger. Each of these facets may represent distinct anger-related risk factors for suicidal behavior. The physiological arousal facet may be particularly relevant for predicting suicide attempts among individuals with a tendency to experience intense somatic responses to anger, based on research suggesting that suicidal behavior can bring temporary relief from states of heightened arousal (Haines, Williams, Brain, & Wilson, 1995; Nock & Mendes, 2008). Conversely, the angry behavior facet may confer risk for suicide attempts via etiological mechanisms it shares with risk for other-directed violence, such as trait impulsivity, negative emotionality, and serotonin and dopamine dysfunction (Douglas et al., 2008; Seo, Patrick, & Kennealy, 2008; Verona et al., 2001). Additionally, high levels of hostile attributions and attitudes may increase risk for suicide-related behavior through a cognitive vulnerability to repetitive and ruminative thinking similar to that observed in depression. Thus, the anger facets may index heterogeneity in the mechanisms by which anger confers risk for suicide-related behavior, and examining their unique relationships with suicide attempts could provide insight into when a particular component of anger may be most relevant for assessing risk.
One factor that may moderate relationships between facets of anger and suicide-related behavior is trauma history. According to Van Orden, Joiner, and colleagues (Van Orden et al., 2010; Van Orden, Witte, Gordon, Bender, & Joiner, 2008), exposure to traumatic experiences increases risk for suicidal behavior by promoting habituation to fear and physical pain that is necessary for enacting lethal self-injury. Consistent with this theory, a meta-analysis of 37 studies found a mean Cohen’s d effect size of .44 between sexual abuse history and self-directed violence (broadly defined as recurrent suicidal ideation, plans, attempts, or nonsuicidal self-injury; Paolucci, Genuis, & Violato, 2001). Research also indicates that anger shows moderately strong relationships with exposure to traumatic events (Briere & Runtz, 1987; Neumann, Houskamp, Pollock, & Briere, 1996), suggesting that it is a potentially important predictor of self-directed violence in individuals with a trauma history. Novaco, Chemtob, and colleagues theorized that anger is activated as part of a “survival mode” of functioning related to the fight–flight–freeze response following exposure to a traumatic event (Chemtob, Novaco, Hamada, Gross, & Smith, 1997; Novaco & Chemtob, 1998). Events experienced as traumatic are theorized to promote anger by activating dispositions toward heightened physiological arousal, interpreting situations as threatening and engaging in angry behavior (Novaco & Chemtob, 1998). Among the anger facets, the physiological arousal facet shows the strongest relationship with symptoms of posttraumatic stress (Novaco & Chemtob, 2002) and is positively associated with retrospective reports of childhood abuse in community samples (Kendra, Bell, & Guimond, 2012).
Sexual victimization history, in particular, has been linked to heightened anger and suicide-related behavior (Briere & Runtz, 1987; Neumann, Houskamp, Pollock, & Briere, 1996; Paolucci et al., 2001). Further, it may be crucial to understanding risk for suicide attempts in psychiatric patients, given their heightened risk for childhood sexual victimization (Bryer, Nelson, Miller, & Krol, 1987). Research also suggests that sexual victimization may be a particularly salient risk factor for suicide attempts by females (Roy & Janal, 2006; Soloff, Lynch, & Kelly, 2002), partly as a consequence of the higher prevalence of sexual victimization reported by women and girls than men and boys (Beitchman et al., 1992; Martin, Bergen, Richardson, Roeger, & Allison, 2004). Strikingly, the prevalence of sexual victimization is estimated to be 1.5 to 3 times greater in female than male samples (Briere & Elliott, 2003; Finkelhor, 1994; Finkelhor, Hotaling, Lewis, & Smith, 1990). It is important to note that sexual abuse is not necessarily a stronger predictor of suicide attempts in women than men (Molnar, Berkman, & Buka, 2001; Paolucci et al., 2001). Rather, it is the higher prevalence of sexual victimization among females that makes it a particularly important context to consider in relation to suicide attempts in women.
Gender differences in the tendency for men and women to manifest anger inwardly or outwardly (e.g., Verona & Curtin, 2006) may also be relevant for understanding variability in anger relationships with suicide attempts. There is preliminary evidence to suggest that the expression of anger relates differentially to suicide attempts in men and women. A 13-year prospective study of 180 adolescents followed into young adulthood found that high levels of trait anger and outward expressions of anger (e.g., angry behavior) predicted suicide attempts above a diagnosis of major depressive disorder selectively in men, whereas there were no direct effects of anger on suicide attempts in women (Daniel et al., 2009). Proactive aggression was also associated with suicide attempts in male but not female patients in substance-dependence treatment (Conner, Swogger, & Houston, 2009). In contrast, trait anger was not associated with suicide attempts in male offenders when entered into a model with depressive symptoms (Sadeh, Javdani, Finy, & Verona, 2011). Hostile attitudes and attributions were, however, positively associated with suicide attempts in female offenders (Sadeh et al., 2011). Overall, these studies suggest that tendencies toward expressing anger behaviorally may be more predictive of suicide attempts in men, whereas tendencies toward internalized manifestations of anger (e.g., hostile cognitions and attitudes) may be more predictive of suicide attempts in women. Though few, these studies indicate that gender is a relevant moderator to consider when examining anger as a risk factor for suicide attempts.
On the basis of the literature reviewed, in our study we sought to clarify anger relationships with suicide risk by examining anger as a multidimensional construct (a disposition toward physiological arousal, hostile cognitions, and angry behavior) as well as examining childhood sexual victimization and gender as theoretically relevant moderating variables. We hypothesized that gender and sexual victimization would moderate associations of the anger facets with suicide attempts by psychiatric patients in the year following hospital discharge.
First, we expected childhood sexual victimization to strengthen relationships of the anger facets with suicide attempts, based on research indicating that a history of childhood sexual abuse increases anger symptoms (Neumann et al., 1996). We expected this moderation to be particularly strong in relation to the physiological arousal facet, given research linking trauma with physiological arousal and physiological arousal with suicidal behavior (Haines et al., 1995; Kendra et al., 2012; Novaco & Chemtob, 2002). In regard to gender differences, we predicted that suicide attempts in male psychiatric patients would be more strongly associated with the tendency to express anger behaviorally (Conner et al., 2009; Daniel et al., 2009), whereas suicide attempts in female psychiatric patients would be more strongly associated with the hostile cognition facet of anger (Sadeh et al., 2011). We did not make predictions about whether gender would moderate relationships of the physiological arousal facet with suicide attempts, given the lack of previous research on this topic. To assess whether facets of anger predicted above the variance already accounted for by well-established predictors of self-directed violence, we included a measure of depression and anxiety symptoms and recent suicide attempts in the 2 months prior to hospitalization as covariates in analyses.
Method Sample and Participant Selection
Participants were drawn from the MacArthur Violence Risk Assessment Study (MVRS), a longitudinal study of psychiatric patients recruited while hospitalized in one of three acute inpatient facilities (N = 1,136, see Monahan et al., 2001, for a more detailed description of the study methods). Participants were sampled based on age, gender, and ethnicity to ensure a consistent distribution of participants with these characteristics across the three recruitment sites. Individuals who spoke English and received a medical record diagnosis of one or more of the following diagnoses were eligible to participate: schizophrenia, schizoaffective disorder, schizophreniform disorder, dysthymia, mania, major depression, brief reactive psychosis, alcohol abuse, alcohol dependence, substance abuse, substance dependence, delusional disorder, or personality disorder (i.e., only when an Axis I diagnosis was not present). Chart diagnoses were verified by an interview with a research clinician using the Diagnostic and Statistical Manual of Mental Disorders (3rd ed., rev.; DSM–III–R; American Psychiatric Association, 1987) Checklist or Structured Interview for DSM–III–R (SCID) Personality and corresponded with the chart diagnosis in 85.7% of the cases. Discrepancies in diagnosis between the chart and research clinician were resolved by a consultant psychiatrist at each site. The prevalence rates for the diagnoses in the present sample are comparable to nationally representative samples of inpatient discharges (e.g., Banta, Belk, Newton, & Sherzai, 2010). Of the 1,695 eligible participants approached to participate, 71% agreed to participate in the study. Participants completed an initial assessment during the hospital stay and were reassessed every 10 weeks for the year following discharge. All participants were approached to participate in the study within 21 days of hospital admission, and the average amount of time between hospital admission and invitation to participate in the study was 4.5 days. The initial assessment was conducted at any point during the hospital stay, and the median length of hospitalization was 9 days.
This study involved analysis of data collected in the MVRS project, which has been made publically available by the MacArthur Research Network on Mental Health and the Law. Descriptions of how informed consent was obtained and institutional review board approvals for the MVRS are provided elsewhere (Monahan et al., 2001; Steadman et al., 1998). Additional institutional review board approval was not necessary for the present project, as it involved secondary analysis of a publically available, deidentified dataset.
During hospitalization, participants completed the Novaco Anger Scale (Novaco, 1994) questionnaire, and a structured clinical interview was conducted to assess history of childhood sexual abuse and each participant’s psychiatric symptoms on the Brief Psychiatric Rating Scale (BPRS; Overall & Gorham, 1962). Engagement in a suicide attempt was reassessed every 10 weeks in the year following discharge from the hospital. The number of participants with missing data at each follow-up assessment was as follows: 10-week assessment = 99 (11%); 20-week assessment = 113 (12%); 30-week assessment = 164 (18%); 40-week assessment = 185 (21%); 50-week assessment = 186 (21%). During the year posthospitalization, 533 (59%) participants completed all of the follow-up assessments, 159 (18%) participants missed one assessment, 81 (9%) participants missed two assessments, 70 (8%) participants missed three assessments, and 54 (6%) participants missed four assessments. To be included in the present study, participants must have (a) completed all of the measures administered during hospitalization, (b) completed at least three follow-up assessments in the year following hospital discharge, and (c) completed either the 40-week or 50-week follow-up assessment. These inclusion criteria resulted in 93.9% of the sample with data at the final 50-week follow-up assessment and 92.6% of the sample with at least four out of five follow-up assessments completed. Participants lost due to incomplete follow-up assessments were more likely to be male, score higher on dispositions toward angry behavior, score lower on symptoms of depression and anxiety, and were less likely to report childhood sexual victimization. Despite these differences, the effect of attrition on the present findings is likely minimal because the variables the groups differ on are included in the model as predictors and supplemental analyses indicate that results do not change when these individuals are included in analyses versus when they are removed.
The final sample consisted of 748 male (55.6%) and female (44.4%) psychiatric patients ages 18 to 40 (M = 30.0; SD = 6.23). The majority of participants self-identified as White (69.4%), followed by African-American (28.6%), and Hispanic (2%). Alcohol and substance use disorders were the most common diagnoses, n = 557, 74.5% (alcohol abuse: n = 117, 15.6%; alcohol dependence: n = 368, 49.2%; substance abuse: n = 277, 37.0%; substance dependence: n = 368, 49.2%), followed by mood disorders, n = 505, 67.5% (major depression: n = 428, 57.2%; dysthymia: n = 22, 2.9%; bipolar disorder: n = 104, 13.9%; mania: n = 65, 8.7%), and psychotic disorders, n = 145, 19.4% (schizophrenia: n = 115, 15.4%; schizoaffective disorder: n = 44, 5.9%; schizophreniform disorder: n = 2, 0.3%; brief reactive psychosis: n = 4, 0.5%, delusional disorder: n = 4, 0.5%). (Total exceeds 100% due to comorbidity.) Approximately 37% of the sample reported engaging in self-injurious behavior in the two months prior to hospital admission, and approximately 20% of participants reported inflicting self-injury with the intent to cause death (i.e., a suicide attempt).
Assessments and Measures
Facets of anger
The Novaco Anger Scale (NAS; Novaco, 1994) is a self-report questionnaire that was used to index facets of anger disposition related to physiological arousal, hostile cognitions, and angry behavior. The NAS shows good reliability and construct validity in psychiatric, forensic, and nonclinical samples (Hornsveld, Muris, & Kraaimaat, 2011; Jones, Thomas-Peter, & Trout, 1999; Mills, Kroner, & Forth, 1998; Novaco, 1994). Research indicates the NAS total and subscale scores show adequate 1-month test–retest reliability (e.g., rs > .78), concurrent validity with other anger measures (e.g., Aggression Questionnaire, Anger Expression Scale, State-Trait Anger Expression Inventory), and internal consistencies (e.g., alpha coefficients > .80 for the subscales) (Hornsveld et al., 2011; Jones et al., 1999; Mills et al., 1998; Novaco & Chemtob, 2002). Physiological arousal was measured using the 16-item NAS Arousal subscale that assesses physiological activation and readiness for action, including anger intensity, duration, somatic tension, and irritability (M = 37.2, SD = 6.4; Cronbach’s α = .89 for present sample). Disposition toward angry behavior was measured using the 16-item NAS Behavior subscale, which describes behavioral manifestations of anger including impulsive reaction, verbal aggression, physical confrontation, and indirect expression of aggression (M = 29.8, SD = 6.9; Cronbach’s α = .89 for present sample). Hostile cognitions and attitudes were assessed with the 16-item NAS Cognitive subscale, which indexes thought processes that initiate and maintain anger, including attentional focus, suspiciousness, rumination, and hostile attitudes (M = 31.7, SD = 5.2; Cronbach’s α = .79 for present sample). For each item, participants rated how true a statement was of their thoughts, feelings, and behavior on a scale from 1 (never true) to 3 (always true), and items were summed to create the three subscales. Participants completed the NAS during hospitalization, and the Arousal, Behavior, and Cognitive subscales were entered into analyses as hypothesized predictors of later suicide attempts in the year following hospital discharge.
Childhood sexual victimization
During hospitalization, participants were asked “Did anyone ever sexually abuse or assault you?” and participants who endorsed a history of sexual victimization were asked to provide the age at which the sexual abuse or assault first took place. A dichotomous sexual victimization variable was created for analysis based on the presence or absence of sexual abuse or assault before age 18 (1 = history of childhood sexual abuse or assault, 0 = absence of childhood sexual abuse or assault). Approximately 40% of the sample reported a history of childhood sexual victimization (n = 318). Participants were then asked to classify the type of sexual victimization that occurred, in categories that correspond to definitions of sexual violence and victimization used by the Centers for Disease Control (2002). The prevalence of sexual victimization reported in the final sample was 42.5%. The most commonly reported form of sexual victimization was forced sexual intercourse (28.3%), followed by inappropriate touching (21.3%), sodomy (19.1%), attempted intercourse (12.0%), and oral sex (10.6%). Approximately 40% of the participants who endorsed a history of experiencing sexual violence reported that abuse happened “frequently” or “too many times to count,” 12% reported it happened “sometimes,” and another 40% reported it happened “once” or “twice.” Childhood sexual victimization was measured during hospitalization and entered into analyses as a hypothesized predictor of later suicide attempts in the year following hospital discharge.
Depression & anxiety symptoms
Severity of depression and anxiety was assessed with the Brief Psychiatric Rating Scale (BPRS; Overall & Gorham, 1962), a widely used clinician rating scale of psychiatric symptoms. Clinicians rated participants on the severity of symptoms experienced in the previous week on a scale from 1 (none reported) to 7 (very severe) at the time of the assessment, which occurred during hospitalization. On the basis of factor analyses of the BPRS (Overall, Hollister, & Pichot, 1967; Shafer, 2005), a Depression and Anxiety symptom subscale was constructed by summing symptoms of depressive mood, guilt feelings, and anxiety (M = 10.7, SD = 4.6). The Depression and Anxiety BPRS subscale was entered as a covariate in analyses of suicide attempts after hospital discharge, based on research indicating symptoms of depression and anxiety are strong predictors of suicide attempts (Goldston, Reboussin, & Daniel, 2006; Kessler, Borges, & Walters, 1999; Sareen et al., 2005).
Recent suicide attempts (2-month history)
Given that a history of attempting suicide is one of the best predictors of future suicide attempts (Goldston et al., 1999; Joiner et al., 2005), we included a measure of suicide attempts in the 2 months prior to hospital admission as a covariate in analyses that predicted later suicide attempts during the year after hospital discharge. Consistent with the recommendations of Silverman et al., (2007), a suicide attempt was operationalized as a self-inflicted, potentially injurious behavior with a nonfatal outcome for which there is evidence of intent to die, which was assessed via self-report. Recent suicide attempts (2-month history) were assessed by asking each participant if he or she had attempted to hurt himself or herself with the intention of killing himself or herself in the 2 months prior to hospital admission. For purposes of analysis, suicide attempts were coded as a dichotomous variable (1 = recent suicide attempt present, 0 = recent suicide attempt absent). Approximately 20% of the sample reported a recent suicide attempt in the 2 months prior to hospitalization (n = 148). The recent suicide attempt (2-month history) variable was entered as a covariate in analyses of suicide attempts after discharge to the community.
Suicide attempts in the year following hospital discharge
At each 10-week assessment in the year following hospital discharge, participants were asked if they attempted to hurt themselves in the period since the previous assessment. Participants who reported an attempt to hurt themselves were then asked to specify the degree of harm sought by the act. A suicide attempt was operationalized as engaging in an act of self-injury with the intent to die, based on the nomenclature recommended by a panel of experts in suicidology (Silverman et al., 2007). Given that suicide attempts are low base-rate behaviors, suicide attempts over the course of the year following hospital discharge were aggregated into a single dependent variable that was coded dichotomously (1 = suicide attempt present; 0 = suicide attempt absent). The rates of suicide attempts at each follow-up assessment ranged from 3.7% to 5.3% of the final sample (10 week = 5.3%, 20 week = 4.0%, 30 week = 4.4%, 40 week = 3.6%, 50 week = 3.7%). Overall, approximately 17% of the sample reported engaging in a suicide attempt in the year following hospitalization (n = 124).
Data Analysis
Bivariate associations were assessed between (a) dichotomous variables with Phi coefficients and (b) a dichotomous and a continuous variable with point-biserial correlations, and (c) continuous variables with Pearson correlation coefficients. Univariate gender differences were assessed via independent samples t tests or chi-square analysis, depending on the whether the variable assessed was continuous or dichotomous. Hypothesized relationships among the study variables were examined in multiple logistic regression analyses. Gender, childhood sexual victimization, the NAS subscales (Arousal, Behavioral, Cognitive), and their interactions were entered as predictors of whether or not participants attempted suicide during the year following hospital discharge. To examine the influence of the hypothesized predictors above that of previously established predictors of suicide attempts, specifically symptoms of depression and anxiety, and recent suicide attempts (2-month history), these variables were included as covariates in the regression analyses in addition to age. The number of missing follow-up assessments posthospitalization and the period of time assessed posthospitalization were also included as covariates to account for variation in the posthospitalization assessment period across participants, but they were not hypothesized to predict future suicide attempts. Assessment of multicollinearity indicated that predictor intercorrelations (< .80; Leahy, 2000) and tolerance levels (> .20; Gaur & Gaur, 2006) were within acceptable ranges. Odds ratios were calculated to provide a measure of effect size. Predictors and covariates were z-scored before they were entered into the regression model. The figures were created by plotting the predicted probabilities of attempting suicide for each participant against his or her score on the relevant NAS subscale.
Results Descriptive Statistics
Bivariate correlations between the hypothesized covariates, predictors, and suicide attempts in the year following hospital discharge are provided in Table 1 as a reference for subsequent multivariate analyses. The NAS subscales showed moderate to strong positive interrelationships, which indicate they index related but distinguishable facets of anger. Among the anger facets, the tendency to experience physiological arousal and activation (NAS Arousal) correlated most strongly with the occurrence of suicide attempts in the year following hospital discharge and childhood sexual victimization. Recent suicide attempts (2-month history) and childhood sexual victimization also showed the expected positive relationships with the likelihood of suicide attempts in the year following hospital discharge.
Bivariate Relationships Among Covariates, Predictors, and Suicide Attempts in the Year Following Hospital Discharge
Table 2 includes descriptive statistics for the hypothesized covariates, predictors, and suicide attempts in the year following hospital discharge in the total sample and for women and men separately. Approximately 17% of the sample reported one or more suicide attempts in the year following discharge from the hospital. Proportionately more women reported suicide attempts in the 2 months prior to hospitalization than men, χ2(1) = 5.17, p = .027, but no gender differences emerged in the risk of suicide attempts during the year follow-up period. Clinicians rated women as having more severe symptoms of depression and anxiety on the BPRS, t(746) = − 4.10, p < .001, and women reported higher levels of physiological arousal on the NAS than men, t(746) = −2.94, p = .003. Women did not differ from men on the hostile cognitions or angry behavior NAS facets. As predicted, the genders differed by childhood sexual victimization history, with a substantially larger proportion of women than men reporting sexual abuse or assault as children (62.3%) than men (26.7%), χ2(1) = 96.1, p < .001.
Descriptive Statistics for the Final Sample and by Gender
Multiple Logistic Regressions
Results of regressing suicide attempts in the year following hospital discharge on gender, childhood sexual victimization, and the NAS subscales are presented in Table 3. A history of suicide attempts in the 2 months before hospital admission, Wald χ2 = 14.13, p < .001, odds ratio (OR) = 1.40, predicted the risk of attempting suicide after hospital discharge, whereas BPRS Depression and Anxiety Symptoms and age did not. Childhood sexual victimization also positively predicted attempting suicide in the year following hospital discharge, Wald χ2 = 7.84, p = .005, OR = 1.36. In the multivariate model, gender and the NAS subscales did not directly predict risk of attempting suicide above the variance accounted for by recent suicide attempts (2-month history), age, and symptoms of depression and anxiety during hospitalization.
Logistic Regression Models of Gender, Childhood Sexual Victimization, and Anger Facets Predicting Suicide Attempts in the Year Following Hospital Discharge
Gender and childhood sexual victimization moderated the relationships of the NAS subscales with risk of attempting suicide during the year follow-up period. First, a two-way Gender × NAS Behavior interaction emerged, Wald χ2 = 7.92, p = .005, OR = 0.61, that reflected a cross-over effect. Specifically, a disposition to engage in angry behavior positively predicted suicide attempts in men and negatively in women. These relationships were further qualified by a Gender × Childhood Sexual Victimization × NAS Behavior interaction, Wald χ2 = 6.87, p = .009, OR = .63, which is depicted in Figure 1. Follow-up analyses conducted within each gender as a function of childhood sexual victimization revealed that NAS Behavior significantly predicted suicide attempts in the year posthospital discharge for men, Wald χ2 = 5.50, p = .019, OR = 1.76, but not women (p > .15). In men with childhood sexual victimization, a tendency to act aggressively when angry positively predicted suicide attempts, Wald χ2 = 6.98, p = .008, OR = 3.26, whereas it did not in men without a history of sexual abuse or assault (p >.84).
Figure 1. NAS Behavior Predicting the Probability of Suicide Attempts in the Year Following Hospital Discharge. CSV = Childhood Sexual Victimization.
The regression analysis also produced a Gender × Childhood Sexual Victimization × NAS Arousal interaction, Wald χ2 = 4.03, p = .045, OR = 1.53. To disentangle the interaction, follow-up analyses were conducted within each gender. These analyses revealed a significant Childhood Sexual Victimization × NAS Arousal interaction for women, Wald χ2 = 4.29, p = .038, OR = 1.95, but not men (p > .46). As illustrated in Figure 2, physiological arousal positively predicted suicide attempts in women with childhood sexual victimization, Wald χ2 = 4.01, p = .045, OR = 2. 03, but did not in women without childhood sexual victimization (p > .34).
Figure 2. NAS Arousal Predicting the Probability of Suicide Attempts in the Year Following Hospital Discharge. CSV = Childhood Sexual Victimization.
In summary, the NAS facets differentially predicted suicide attempts in the year following hospital discharge as a function of gender and childhood sexual victimization above the variance accounted for by symptoms of depression and anxiety and recent suicide attempts. A disposition toward angry behavior predicted a greater likelihood of suicide attempts in men, particularly men with a history of childhood sexual victimization. In contrast, the arousal facet of anger was particularly important for predicting suicide attempts in women with a history of childhood sexual abuse or assault.
DiscussionThis study investigated whether facets of anger predict suicide attempts in the year following patients’ discharge from psychiatric hospitalization. As hypothesized, facets of anger predicted suicide attempts above the influence of other well-established risk factors, including symptoms of depression and anxiety, and a recent history of suicide attempts, when entered into a model that accounted for the moderating effects of gender and sexual victimization history. Physiological arousal predicted suicide attempts in women who experienced childhood sexual victimization. In contrast, a disposition toward angry behavior predicted suicide attempts in men, particularly those with childhood sexual victimization. In combination, the results suggest that facets of anger have distinct predictive relationships with risk for suicide attempts in the year following hospital discharge. Further, the results illustrate the importance of incorporating gender and sexual victimization into models of risk for self-directed violence.
The association of a disposition toward physiological arousal with increased risk for suicide attempts in the context of sexual victimization extends the growing literature that indicates heightened physiological arousal to stress is a risk factor for suicide and nonsuicidal self-injury (Nock, 2009; Nock & Mendes, 2008). Researchers have theorized that one function of engaging in suicidal behavior is to regulate aversive arousal and negative affect (M. Z. Brown et al., 2002; Nock & Mendes, 2008). However, few studies have directly tested the hypothesis that a disposition toward physiological arousal predicts future suicide-related behavior. Recent work has found that individuals with a history of nonsuicidal self-injury demonstrate increased skin conductance during a distressing task (Nock & Mendes, 2008), and arousal decreases in self-injurers when they imagine engaging in suicidal behavior (Haines et al., 1995). The present results extend these studies by showing that patients with a history of sexual victimization had a greater likelihood of suicide attempts following hospital discharge when they reported increased anger-related arousal, an effect that was particularly strong for women.
In contrast to the results for women, the findings for men indicate that a disposition toward angry behavior predicts risk for suicide attempts following hospital discharge. This finding replicates previous research showing that angry behavior predicts suicide attempts in young adult men but not women (Daniel et al., 2009) and proactive aggression predicts suicide attempts in male but not female patients in substance-dependence treatment (Conner et al., 2009). A disposition toward angry behavior may be more predictive of suicide attempts in men than women as a function of their tendency to have lower levels of behavioral constraint, a personality trait positively associated with angry behavior and suicide attempts (Douglas et al., 2008; Roberts, Caspi, & Moffitt, 2001; Verona et al., 2001).
We did not find that hostile cognitions are a stronger predictor of suicide attempts in women than men, which we expected based on previous work examining these relationships in an externalizing sample of adults (Sadeh et al., 2011). This failure to replicate may reflect differences in the sample composition (i.e., individuals involved in the criminal justice system vs. nonforensic psychiatric patients), though more research is needed to clarify relationships between gender, hostility, and risk for suicidal behavior. Although the present findings extend the growing literature on gender differences in risk factors for self-directed violence, more research is needed to better understand the mechanisms underlying the relationship between anger facets and risk for suicide attempts in men and women.
One contribution of this study that has implications for clinical practice is that it provides preliminary evidence that men and women with a history of sexual victimization may benefit from different treatment interventions. First, the risk conferred by physiological arousal for future suicide attempts in women supports the clinical relevance of teaching at-risk individuals ways to tolerate intense distress and aversive arousal, which is typically elevated among individuals with a history of trauma exposure (Kendra et al., 2012; Novaco & Chemtob, 2002). Thus, the present results support the potential value of the distress tolerance skills taught in dialectical behavior therapy and relaxation-based interventions for reducing risk for suicide attempts, particularly for women who report high levels of physiological arousal. Second, the finding that a disposition toward angry behavior is a predictor of suicide attempts in men with childhood sexual victimization suggests that anger management may reduce risk of suicide attempts in men with a history of sexual abuse. Indeed, meta-analyses conducted on the effectiveness of anger management interventions (e.g., cognitive restructuring, relaxation, skills training) indicate that they are effective at reducing anger and decreasing aggression (Beck & Fernandez, 1998; DiGiuseppe & Tafrate, 2003), though the potential of these interventions to reduce risk for suicide attempts has largely been neglected in the literature. Taken together, our findings suggest that teaching distress tolerance and relaxation skills may help reduce risk for suicide attempts in female psychiatric patients, whereas anger management techniques may help mitigate risk of suicide attempts in male psychiatric patients with a history of sexual victimization.
As with any investigation, this study has potential limitations. First, risk factors for suicide deaths were not studied, which may limit the generalizability of these results to nonfatal suicide attempts. Nonetheless, research indicates that nonfatal suicide attempts are among the strongest predictors of eventual suicide death (Borges et al., 2006), suggesting that the present results may still be relevant to understanding risk for suicide death. Second, our use of a retrospective self-report assessment of sexual victimization experiences is susceptible to recall and social-desirability bias, which may have increased measurement error and decreased our ability to detect certain relationships between sexual victimization and suicide attempts. Research examining the limitations associated with using retrospective measures of adverse events in childhood suggests that this methodological approach has measurement bias, but it can provide relevant information as long as the events assessed are adequately operationalized, do not rely on detailed accounts, and are serious enough to be recalled (Hardt & Rutter, 2004). Measurement of sexual victimization in this study meets these requirements, suggesting it is a useful measure despite the bias inherent in use of a retrospective measure of childhood adversity. Third, the longitudinal nature of this study may have impacted the likelihood that patients would attempt suicide, as there is some evidence that follow-up contacts with patients can influence the risk of suicide-related behavior (Kim et al., 2010; Motto & Bostom, 2001). Also, there is a possibility that attrition affected the findings, given that not all participants completed the follow-up assessments. Fourth, our measure of suicide attempts was based on self-report of the participants and data were not available to assess the reliability of this variable.
We also did not include psychiatric diagnosis as a moderating variable in this study and cannot speak to how diagnostic status may have affected our findings. It may be fruitful to explore whether the anger facets function differentially across psychiatric disorders in future research. For instance, anger may function differently in internalizing (e.g., mood and anxiety disorders) versus externalizing (e.g., substance use and antisocial) disorders in that the former may confer risk for an individual to direct anger inward, whereas the latter may increase the likelihood that an individual will direct anger toward others. However, research suggests that externalizing disorders are associated with risk for suicide attempts when comorbidity with internalizing disorders is accounted for (Verona, Sachs-Ericsson, & Joiner, 2004), which is not consistent with this model. Serotonin dysfunction and poor self-regulation are mechanisms by which anger may increase risk for suicide attempts in psychiatric patients across diagnoses (Douglas et al., 2008; Seo et al., 2008), as empirical evidence converges to suggest that low serotonin functioning and impaired executive functions are associated with both depression and impulsive aggression (Carver, Johnson, & Joormann, 2008). On a related note, it is possible that facets of anger could indirectly index factors that are not specific to anger, but encompass different risk processes than those typically captured by a unitary anger variable, such as trauma reexperiencing or other psychiatric diagnoses. Thus, the interpretation of the findings for the anger facets may not generalize to studies that have examined a more general anger variable. Further research is needed to explicate the external correlates of these facets in relation to anger-related psychiatric diagnoses and risk for suicide attempts.
This study has several strengths, including the use of a prospective design to evaluate risk factors for future suicide attempts, recruitment of a large and clinically relevant sample of psychiatric patients at elevated risk for suicide (Qin, Agerbo, & Mortensen, 2003), and examination of an integrative model of risk for suicide attempts. It extends the literature on risk for self-directed violence by examining anger as a multidimensional construct and adds to the burgeoning body of research that indicates gender and childhood victimization experiences are important moderators to consider when assessing risk for suicide attempts.
In summary, the results of our study support the conclusion that facets of anger and childhood sexual victimization increase the risk of suicide attempts by psychiatric patients after discharge to the community, and that assessment of these issues adds information over and above other well-established risk factors for suicidal behavior. The results suggest that dispositions toward physiological arousal and angry behavior differentially affect risk for attempted suicide in female versus male patients, respectively. Furthermore, the findings indicate that relationships between facets of anger and suicide attempts are strengthened in the context of childhood sexual victimization.
Footnotes 1 Swogger et al. (2012) also investigated self-directed violence (defined as any attempt to hurt oneself with or without suicidal intent) in relation to trait anger in the MacArthur Violence Risk Assessment Study. The present study expands on Swogger et al. by examining how gender and sexual victimization moderate relationships of the NAS anger facets with suicide attempts in the year following hospital discharge.
2 Supplemental analyses were conducted with the most prevalent diagnoses in the sample (i.e., major depression, alcohol abuse–dependence, substance abuse–dependence, and schizophrenia) to examine whether psychiatric diagnosis moderated the results. At the bivariate level, NAS Arousal correlated positively with major depressive disorder (r = .17, p = .001), alcohol abuse–dependence (r = .11, p = .002), and substance dependence (r = .11, p = .002), and negatively with schizophrenia (r = −.15, p = .001). NAS Behavior correlated positively with alcohol abuse–dependence (r = .18, p = .001) and substance dependence (r = .16, p = .001) and was unrelated to major depressive disorder and schizophrenia. NAS Cognitive correlated positively with alcohol abuse–dependence (r = .17, p = .001) and substance dependence (r = .13, p = .001) and was unrelated to major depressive disorder and schizophrenia. The unique and interactive effects of each diagnosis in the prediction of future suicide attempts were assessed in separate logistic regression analyses. A diagnosis of alcohol abuse or dependence predicted a greater likelihood of future suicide attempts, Wald χ2 = 4.26, p = .039, OR = 1.24, whereas a diagnosis of schizophrenia predicted a decreased likelihood of future suicide attempts, Wald χ2 = 12.1, p = .001, OR = 0.56. It is important to note that none of the diagnoses moderated the results reported in this study or produced new findings.
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Submitted: August 12, 2012 Revised: March 22, 2013 Accepted: March 22, 2013
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Source: Journal of Abnormal Psychology. Vol. 122. (3), Aug, 2013 pp. 879-890)
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- Genetic and environmental bases of childhood antisocial behavior: A multi-informant twin study.
- Authors:
- Baker, Laura A.. Department of Psychology, University of Southern California, Los Angeles, CA, US, lbaker@usc.edu
Jacobson, Kristen C.. Department of Psychiatry, University of Chicago, Chicago, IL, US
Raine, Adrian. Department of Psychology, University of Southern California, Los Angeles, CA, US
Lozano, Dora Isabel. Department of Psychology, University of Southern California, Los Angeles, CA, US
Bezdjian, Serena. Department of Psychology, University of Southern California, Los Angeles, CA, US - Address:
- Baker, Laura A., Department of Psychology, University of Southern California, Los Angeles, CA, US, 90089-1061, lbaker@usc.edu
- Source:
- Journal of Abnormal Psychology, Vol 116(2), May, 2007. pp. 219-235.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 17
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- antisocial behavior, aggression, genes, environment
- Abstract:
- Genetic and environmental influences on childhood antisocial and aggressive behavior (ASB) during childhood were examined in 9- to 10-year-old twins, using a multi-informant approach. The sample (605 families of twins or triplets) was socioeconomically and ethnically diverse, representative of the culturally diverse urban population in Southern California. Measures of ASB included symptom counts for conduct disorder, ratings of aggression, delinquency, and psychopathic traits obtained through child self-reports, teacher, and caregiver ratings. Multivariate analysis revealed a common ASB factor across informants that was strongly heritable (heritability was .96), highlighting the importance of a broad, general measure obtained from multiple sources as a plausible construct for future investigations of specific genetic mechanisms in ASB. The best fitting multivariate model required informant-specific genetic, environmental, and rater effects for variation in observed ASB measures. The results suggest that parents, children, and teachers have only a partly 'shared view' and that the additional factors that influence the 'rater-specific' view of the child's antisocial behavior vary for different informants. This is the first study to demonstrate strong heritable effects on ASB in ethnically and economically diverse samples. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Aggressive Behavior; *Antisocial Behavior; *Environment; *Genetics; Twins
- Medical Subject Headings (MeSH):
- Aggression; Antisocial Personality Disorder; Child; Conduct Disorder; Diseases in Twins; Female; Humans; Juvenile Delinquency; Longitudinal Studies; Male; Personality Assessment; Risk Factors; Social Environment; South Carolina; Triplets; Twins, Dizygotic; Twins, Monozygotic
- PsycINFO Classification:
- Behavior Disorders & Antisocial Behavior (3230)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Childhood (birth-12 yrs)
School Age (6-12 yrs) - Tests & Measures:
- Diagnostic Interview Schedule for Children--Version IV
Child Psychopathy Scale
Child Behavior Checklist
Childhood Aggression Questionnaire DOI: 10.1037/t20800-000 - Grant Sponsorship:
- Sponsor: National Institute of Mental Health
Grant Number: R01 MH58354
Recipients: Baker, Laura A.
Sponsor: National Institute of Mental Health
Grant Number: K02 MH01114-08
Other Details: Independent Scientist Award
Recipients: Jacobson, Kristen C. - Methodology:
- Empirical Study; Quantitative Study; Twin Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Feb 9, 2007; Revised: Feb 6, 2007; First Submitted: Sep 1, 2005
- Release Date:
- 20070521
- Correction Date:
- 20130520
- Copyright:
- American Psychological Association. 2007
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/0021-843X.116.2.219
- PMID:
- 17516756
- Accession Number:
- 2007-06673-001
- Number of Citations in Source:
- 67
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2007-06673-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2007-06673-001&site=ehost-live">Genetic and environmental bases of childhood antisocial behavior: A multi-informant twin study.</A>
- Database:
- PsycINFO
Genetic and Environmental Bases of Childhood Antisocial Behavior: A Multi-Informant Twin Study
By: Laura A. Baker
Department of Psychology, University of Southern California;
Kristen C. Jacobson
Department of Psychiatry, University of Chicago
Adrian Raine
Department of Psychology, University of Southern California
Dora Isabel Lozano
Department of Psychology, University of Southern California
Serena Bezdjian
Department of Psychology, University of Southern California
Acknowledgement: This study was supported by National Institute of Mental Health (NIMH) Grant NIMH R01 MH58354 to Laura A. Baker and NIMH Independent Scientist Award K02 MH01114-08 to Kristen Jacobson. We wish to thank the University of Southern California twin project staff, for assistance in data collection and scoring, and the twins and their families, for their participation in this research.
Why do some children grow up to be prosocial, law-abiding individuals, whereas others engage in patterns of disruptive, defiant, and delinquent behavior, even falling into the criminal justice system well before reaching adulthood? A plethora of studies have investigated the etiology of such individual differences, with abundant evidence demonstrating the importance of both social circumstances and biological risk factors in antisocial behavior across the life span (Baker, 1999; Raine, 1993, 2002; Raine, Brennan, Farrington, & Mednick, 1997; Stoff, Breiling, & Maser, 1997). Among these risk factors, genetic and environmental influences have been of considerable interest and are likely to play a key role in our understanding of aggression and other antisocial behaviors and, thus, our ability to avert them.
In fact, genetic and environmental influences in aggressive and antisocial behavior (ASB) have been studied extensively. Several early adoption studies in both Scandinavia and the United States have provided the intriguing finding that not only does the risk for adult criminal offending run in families but familial similarity is due primarily to shared genetic risk (Bohman, 1978; Cadoret, 1978; Hutchings & Mednick, 1971; Loehlin, Willerman, & Horn, 1985; Sigvardsson, Cloninger, Bohman, & Von Knorring, 1982). Genetic predispositions have also been shown to play a significant role in the normal variation in adult aggressive behavior, perhaps especially in more impulsive forms (Coccaro, Bergeman, Kavoussi, & Seroczynski, 1997). In contrast, studies that have included adolescents and younger children vary widely in their estimates of the relative importance of genes and environment, with heritability estimates (h2) indicating that genetic effects could explain as little as nil or upward of three fourths of the variance in ASB (see Rhee & Waldman, 2002, for the most recent review).
Using meta-analysis of key behavioral genetic studies in ASB, Rhee and Waldman (2002) found that, combining results across studies, there were significant effects of additive genetic influence (a2 = .32), of nonadditive genetic influences (d2 = .09), and of shared (e2s = .16) and nonshared environment (e2ns = .43). These genetic and environmental effects were found to differ, however, according to the definition and method of assessing ASB, as well as by the age at which ASB was studied. The nonadditive genetic effects appear most strongly for studies of criminal convictions compared with all other definitions of ASB. Shared environmental effects were stronger for parental reports of ASB compared with self-reports and with official records, and these shared environmental effects appear to diminish from childhood to adulthood.
It is also noteworthy, however, that age and method of assessment are confounded across studies—investigations of younger children tend to rely on parent or teacher reports, whereas studies of older adolescents and adults are more apt to use official records or self-report measures of ASB. Thus, the larger effect of shared environment during childhood may be due to greater reliance on parental or teacher ratings. Given these methodological confounds across studies, it is impossible to know the strength of genetic and environmental influences on individual differences in childhood ASB in particular. Additional studies are required to resolve the effects of genes and environment in ASB in children.
Defining Antisocial BehaviorDefinitions of ASB vary widely across studies and include violations of rules and social norms (e.g., lawbreaking), various forms of aggression (e.g., self-defense or other reactive forms and proactive behaviors such as bullying), and serious patterns of disruptive and aggressive behavior such as those observed in clinical disorders like conduct disorder and oppositional defiant disorder in children or antisocial personality disorder in adults. The variability found in the definitions of these key concepts is also found in the methods of measuring ASB; some studies are based on official records such as police arrests, court convictions, or school records, whereas others rely on behavioral ratings provided by parents or teachers or on self-reports about the participant’s own ASB. Each assessment method has its advantages and disadvantages with no one definition or method of assessment being clearly superior.
Nevertheless, in spite of the wide variations in definitions of ASB, as well as the possibility that the relative importance of genetic and environmental factors may vary for different measures (e.g., Eley, Lichtenstein, & Moffitt, 2003; Mednick, Gabrielli, & Hutchings, 1984; also see Rhee & Waldman, 2002, for review), there is also considerable evidence for a general externalizing dimension of problem behavior underlying these various behaviors and tendencies. Similar to the problem/behavior syndrome described earlier by Jessor and Jessor (1977), a broad latent factor has been purported to be a common link among antisocial behavior, substance dependence, and disinhibited personality traits (Krueger et al., 2002). The externalizing dimension has been found to be more continuous than categorical, with shades of gray describing a range of deviant behaviors across individuals (Markon & Krueger, 2005; Young, Stallings, Corley, Krauter, & Hewitt, 2000). Moreover, this common externalizing factor has been shown to have a strong heritability among adolescents (h2 = .80), accounting for much of the covariation among various aspects of antisocial behavior and disinhibition (Krueger et al., 2002). Among adults, there is also evidence for separate genetic factors for internalizing versus externalizing dimensions of psychopathology (Kendler, Prescott, Myers, & Neale, 2003). This general externalizing factor found across many studies may reflect an overall tendency to act in an unconstrained manner, a genetically based characteristic that manifests itself in various ways depending on the environment (Krueger, 2002). The higher heritability found for this externalizing factor compared with heritabilities obtained from studies that have focused on only one type of antisocial behavior suggests that using a composite measure based on different types of antisocial behavior may be a useful method in molecular genetic research.
Informant VariationAnother important aspect to consider when comparing results across studies is the source of the information about ASB. It is well-known that different informants produce different reports of a child’s behavior. Correlations between raters of the same child are typically about .60 between mother and father ratings, .28 between parent and teacher ratings, and .22 between the parent and child ratings (Achenbach, McConaughy, & Howell, 1987). Largely, each rater provides a unique perspective on the child’s behavior. Children would seem to be the most knowledgeable source to report on their own behavior (particularly covert actions) as well as their motivations, although their cognitive development, truthfulness, and social desirability factors may limit the accuracy of their reports. Parents may be more able to objectively report on a child’s externalizing behaviors, although they may be unaware of covert actions or unwilling to report them to researchers. Although teachers’ reports may also have the advantage of greater objectivity, teachers may have limited knowledge of the child’s antisocial behavior, particularly as it may occur outside of classroom or other school settings. Although researchers sometimes combine ratings across reporters in an attempt to increase scale reliability, different etiologies may exist for scales derived from different informants (Bartels et al., 2003, 2004; Saudino & Cherny, 2001). Thus, the best way to model information from multiple informants is to use a multivariate, factor-based approach that allows for both differences and correlations across informants simultaneously (Kraemer et al., 2003).
There are at least three advantages to using a factor-based approach when dealing with multiple informants in twin studies. First, such a model allows for the possibility that there may be different genetic and environmental etiologies depending upon the perspective of the rater. Second, it allows one to explicitly model and test for the significance of certain types of rater bias. Finally, because the underlying common factor will represent (by definition) a “shared view” of antisocial behavior across informants, the heritability of the common factor may be higher than the heritabilities obtained through any one informant. If this is the case, then combining information from different types of reporters may yield stronger genetic signals in molecular genetic studies. Previous studies of ASB in preadolescent children have relied heavily on either parent or teacher reports, although a few studies have obtained data from multiple reporters, most commonly from the mother and the father (e.g., Bartels et al., 2003, 2004; Neale & Stevenson, 1989; Hewitt, Silbert, Neale, Eaves, & Erickson, 1992) or from parent(s) and teachers (e.g., Hudziak et al., 2003; Martin, Scourfield, & McGuffin, 2002; Vierikko, Pulkkinen, Kaprio, & Rose, 2004) and occasionally from parents and children (e.g., Simonoff et al., 1995). We are unaware, however, of any published studies of externalizing disorder that have used reports from caregivers, teachers, and children simultaneously.
Sex DifferencesA final question to consider is whether there are sex differences in the relative importance of genetic and environmental factors for antisocial behavior. In spite of the fact that males are far more likely than females to engage in antisocial, aggressive, and criminal behavior, there are no apparent differences between the sexes in the relative importance of genetic factors (i.e., heritability) in explaining individual differences in antisocial behavior among adults. Heritability of liability toward nonviolent criminality appears equivalent for men and women, in studies of both twins (Cloninger & Gottesman, 1987) and adoptees (Baker, Mack, Moffitt, & Mednick, 1989), although the average genetic predispositions do appear greater for criminal women compared with criminal men (Baker et al., 1989; Sigvardsson et al., 1982). A few studies of childhood and adolescent ASB have examined sex differences in genetic and environmental etiology, although the results are not consistent. Some studies have found genetic effects to be of greater importance in boys and common environment more important in girls using parental ratings (Silberg et al., 1994), whereas others have found the opposite result using retrospective reports for adolescents (Jacobson, Prescott, & Kendler, 2002), and still others have not found sex-specific etiologies (Eley, Lichtenstein, & Stevenson, 1999). Aggregating across studies in their meta-analysis, Rhee and Waldman (2002) found that the relative importance of genetic and environmental factors in ASB does not differ for males and females, although it should be noted that their analyses did not investigate the extent to which sex differences in etiology might vary across development or method of assessment (i.e., rater). Overall, the question about different etiologies of ASB for males and females remains open.
The University of Southern California (USC) Twin Study of Risk Factors for ASBThis is one of the first prospective twin studies of preadolescent children to focus on aggressive and antisocial behavior using a multitrait, multi-informant approach. In this article we present results for the comprehensive phenotypic assessments of aggressive and antisocial behavior conducted during the first wave of the study, while the participants are at the brink of adolescence (ages 9 and 10 years old), and use multivariate genetic factor models to examine the extent to which genetic and environmental influences account for agreement and disagreement across raters. This study expands on previous research in the following important ways. First, it examined the relative influence of genetic and environmental factors on antisocial behavior using an ethnically and socioeconomically diverse sample. The ethnic and socioeconomic variability of the sample may allow for greater generalizability of results to the diverse populations in urban areas, where antisocial, aggressive, and violent behaviors present serious threats to the community at large. Second, it used multiple indices of antisocial behavior. Rather than relying on univariate comparisons of heritability estimates for various types and severities of antisocial behavior, the use of a composite measure based on all of the different indices may yield a stronger genetic signal than any one index of antisocial behavior alone. Third, the study relied on reports of antisocial behavior from multiple informants. This allowed us to (a) examine whether there are significant differences across raters; (b) test formally the extent to which rater bias may influence results; and (c) combine information from different raters in a multivariate model, allowing for the presence of a “shared” view of antisocial behavior that may be more reliable than any single viewpoint. Fourth, our sample consisted of both male and female twins, including opposite-sex pairs, allowing us to examine potential sex differences in the etiology of a shared view of ASB. Finally, it should be noted that although the present results are cross-sectional, they are part of a larger, ongoing longitudinal study. Therefore, in future analyses, we will be able to compare and contrast our results as participants move from the brink of adolescence into adolescence and young adulthood.
Method Overview of the USC Twin Study of Risk Factors for Antisocial Behavior
The USC Twin Study of Risk Factors for Antisocial Behavior is a longitudinal study of the interplay of genetic, environmental, social, and biological factors on the development of antisocial behavior across adolescence. The first wave of assessment occurred during 2001 to 2004, when the twins were 9 to 10 years old, with a 2-year follow-up assessment in the laboratory when twins were ages 11 to 12. Two additional follow-up assessments will be conducted when the twins are ages 14 to 15 (third wave) and 16 to 17 years old (fourth wave). The present analyses are based on data from the first wave. Comprehensive assessment of each child was made, including cognitive, behavioral, psychosocial, and psychophysiological measures based on individual testing and interviews of the child and primary caregiver during the laboratory visit, with additional teacher surveys completed and returned by mail. A detailed description of the study, including a summary of the measures, can be found in Baker, Barton, Lozano, Raine, and Fowler (2006).
Participant Recruitment
The twins and their families who are part of the USC Study of Risk Factors for Antisocial Behavior were recruited from the larger Southern California Twin Register, which contains over 1,400 total pairs of school-age twins born between 1990 and 1995. Participants in the Twin Register are volunteers, and families were ascertained primarily through local schools, both public and private, in Los Angeles and the surrounding communities—see Baker, Barton, and Raine (2002) for a detailed description of the recruitment process and Twin Register from which the twins were sampled. Families identified as having twins in the target age range were sent letters briefly describing the study and inviting them to participate.
Study participation required that the twins be (a) proficient in English and (b) 9 or 10 years old at first assessment (see Baker et al., 2006). In addition, either English or Spanish proficiency was required for the twins’ primary caregiver. Of the 1,400 families who joined the USC Twin Register and were in the target age range, approximately 860 families were contacted by phone to explain the study in greater detail and to schedule a testing session. The sample of 605 tested families thus constituted a 70% participation rate of those families whom we were able to contact. Approximately 30 families (3% of the total eligible sample) did not qualify because of limited English proficiency in the children. The remaining families were either never scheduled, cancelled, or did not show up for their testing session.
Procedure
Laboratory visit protocol
Testing and interviews of the child and caregiver were made during a 6- to 8-hr visit to the USC laboratories. The details of the protocol can be found in Baker et al. (2006). Briefly, the visit included behavioral interviews, neurocognitive testing, social risk factor assessment, and psychophysiological recording of the twins. Caregivers were also interviewed about their twins’ behavior, as well as their own behavior and relationship to each twin. Cheek swab samples were also collected from the participating families in order to extract DNA and test for zygosity.
Participating families were compensated for their visit to USC and provided with additional incentives for keeping scheduled appointments in a timely fashion (total payments were up to $125). Families were also provided with group summaries of study results and individual reports of their twins’ zygosity and each child’s cognitive testing results.
Given the sensitive nature of the information provided by the twins and their caregivers (including illegal behaviors), a Certificate of Confidentiality was obtained for this study from the National Institute of Mental Health to help protect the privacy of the participants. All participants were assured that the information they provided would be coded numerically and not linked to their names and that their individual information would not be shared with anyone outside the research team. The laboratory procedures and all aspects of the study were reviewed by the USC Institutional Review Board and were compliant with federal regulations at the time.
Assessments were conducted by rigorously trained examiners (see Baker et al., 2006, for details). All child interviews were conducted in English; caregiver interviews were conducted in either English (n = 492; 81.3%) or Spanish (n = 113; 18.7%), depending on the language preference of the participant. Less than half of the Hispanic caregivers (44.0%) preferred to be interviewed in Spanish. All caregiver surveys were translated into Spanish and back-translated into English by professional translators.
Teacher surveys
The twins’ teachers were asked to complete surveys about each child’s school behaviors and to return their survey packets to USC in prepaid, addressed envelopes. Teachers were not paid for their participation. Excluding pairs (n = 15) who were either homeschooled or for whom parents felt the teachers did not know their children well enough to rate their child, there was a 60% individual return rate for teacher surveys. Although we did not receive teacher surveys for all twins, we did have information on whether twins were in the same class at school for all but 18 twin pairs. Among the entire sample, 31.4% of twins were in the same classroom. Among the 269 pairs for whom both twins had teacher reports (see the Missing data section for details), 41.4% were in the same classroom at school and were therefore rated by the same teacher. This suggests that teachers were somewhat more willing to return surveys if both twins were in the same class at school. Female–female twin pairs were slightly more likely to be placed in the same classroom than male–male twin pairs (34.8% vs. 31.5%), and monozygotic (MZ) twins were slightly more likely than dizygotic (DZ) twins to be placed in the same class (36.0% vs. 29.0%). However, chi-square analysis revealed that neither of these effects was statistically significant (p = .46 and p = .14, respectively), indicating that our results for teacher reports are unlikely to be biased by differential response patterns.
Sample Characteristics
Participants in the present study consisted of 605 families of twins (n = 596 pairs) or triplets (n = 9 sets) and their primary caregivers who participated in the first wave of assessment in the USC Study of Risk Factors for ASB. To avoid problems of additional familial interdependency associated with the small number of triplet pairs, a single pair consisting of 2 of the 3 triplets was randomly selected for these analyses. The sample was composed of both male and female MZ and DZ pairs, including both same- and opposite-sex DZ twins. Among the 1,219 child participants, there was approximately equal gender distribution with 48.7% boys (n = 594) and 51.3% girls (n = 625); the 605 caregivers were primarily female (94.2%).
Caregiver participants were primarily biological mothers of the twins and triplets (91.4%; n = 553), although other relatives were also interviewed, including biological fathers (n = 35; 5.8%), stepparents (n = 2; 0.3%), adoptive parents (n = 4; 0.7%), grandparents (n = 7; 1.2%), or other relatives (n = 4; 0.6%). At the time of first-wave assessment, nearly two thirds of the children were living with both biological parents, who were either married or living together but unmarried (55.5% and 7.2% of total sample of families, respectively). Among the remaining families in which the biological parents were not living together (because of separation, divorce, death of the parent, or never having been married), the majority of these were not married or living with a partner at the time of first-wave testing—only 6.2% of the total sample was remarried to another partner. Thus, the majority of the children lived in two-parent households, although 114 twin or triplet pairs (18.8%) did live in a single-parent household with no other adult in the home. The remainder of the children (12.2%) resided with a single parent as well as one or more other adults (mostly grandparents).
The child’s ethnicity was determined by the ethnicity of their two biological parents as reported by the primary caregiver. As such, the twin–triplet sample was 26.6% Caucasian (n = 161 pairs), 14.3% Black (n = 86 pairs), 37.5% Hispanic (n = 227 pairs), 4.5% Asian (n = 27 pairs), 16.7% Mixed (n = 101 pairs), and 0.3% other ethnicities (n = 2 pairs). Among the Mixed group, most children (57.4%; n = 58 pairs) had one Hispanic parent, and thus nearly half of the sample (47.1%; n = 281 twin or triplet sets) was of at least partial Hispanic descent. This ethnic distribution is comparable to that in the general Los Angeles population (http://www.census.gov.offcampus.lib.washington.edu/main/www/cen2000.html) and therefore provides a diverse community sample representative of a large urban area.
Median family income was in the $40,000 to $54,000 range (the midpoint of which is $45,500), which is comparable to the median income in Southern California (including Los Angeles, Orange, Riverside, Ventura, and San Bernardino counties) between 2000 and 2002 (average Mdn = $43,042; http://www.census.gov.offcampus.lib.washington.edu/cgi-bin/saipe/saipe.cgi) and the state of California between 2001 and 2003 (average Mdn = $48,979; http://www.census.gov.offcampus.lib.washington.edu/hhes/income/income03/statemhi.html). Education levels, measured on a 6-point scale, ranged from 1 (less than high school) to 6 (post-graduate degree). Maternal and paternal education levels were significantly correlated (r = .61, p < .01), and significantly higher mean levels of education were reported for mothers (M = 3.70, SD = 1.58) than for fathers (M = 3.53, SD = 1.63), t(552) = 3.43, p < .001. A composite measure of both parents’ education levels, occupational status, and family income (Hollingshead, 1975) was used as an index of socioeconomic status (SES) in this study. The distribution of the SES factor was slightly skewed toward higher levels, although there was considerable range in SES in this study.
Zygosity of the same-sex twin pairs was determined for the majority of pairs (398/458 = 87%) through DNA microsatellite analysis (seven or more concordant and zero discordant markers = MZ; one or more discordant markers = DZ). A Twin Similarity Questionnaire (Lykken, 1978) was used to infer zygosity for the remaining 60 pairs for whom adequate DNA samples or results were not available. When both questionnaire and DNA results were available, there was 90% agreement between the two.
The frequencies of the five gender and zygosity groups are presented in Table 1, along with mean age and ethnic distribution. The mean ages during first-wave assessment were 9.60 years (SD = 0.60) for the total sample of children and 40.14 years (SD = 6.61) for their caregivers. Although zygosity groups did not differ in mean age of children at first-wave assessment, F(4, 604) = 0.70, p = .59, there were significant differences in current age of biological mother across groups, F(4, 594) = 3.64, p < .01—mothers of DZ pairs were significantly older compared with mothers of MZ pairs. There was also significant ethnic group variation across these five zygosity groups, χ2(16, N = 605) = 33.82, p < .01—Blacks and Caucasians appeared to be more frequently represented in the DZ groups, whereas a higher percentage of Asians and Hispanics were seen in the MZ groups, particularly among the male participants. These differences may stem from different twinning rates across ethnic groups, due in part to differences in maternal age and use of assisted reproduction methods when conceiving the twins. Although the overall zygosity distribution among same-sex pairs (60.2% MZ) was significantly greater than the expected 50% (p < .01), it was not as markedly high as in most other volunteer samples, in which two thirds of same-sex pairs are typically MZ (Lykken, McGue, & Tellegen, 1987). This sample of children and caregivers appears to be quite representative of both the multiple birth and general population in southern California.
Sample Characteristics
Measures
The present study used a total of 18 different measures of antisocial behavior taken from five different instruments from a total of three unique informants (caregivers, teachers, and children). Instruments varied in terms of their mode of assessment, with some being administered through semistructured interviews (i.e., the Diagnostic Interview Schedule for Children—Version IV [DISC–IV]) and others through questionnaires administered either in an interview format (i.e., the Childhood Aggression Questionnaire [CAQ] and the Child Psychopathy Scale [CPS]) or in paper-and-pencil format (i.e., the Child Behavior Checklist [CBCL]). Each instrument was given to at least two of the three possible informants. The following sections provide detailed information about each of the five instruments, including information about the instrument itself, mode of assessment, informant type, and use of any relevant subscales.
DISC–IV (Schaffer, Fisher, Lucas, Dulcan, & Schwab-Stone, 2000)
The DISC–IV is a highly structured interview designed to assess psychiatric disorders, adapted from the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM–IV; American Psychiatric Association, 1994), and symptoms in children and adolescents ages 6 to 17 years. The DISC was designed to be administered by well-trained lay interviewers for epidemiological research. It has a youth as well as a parallel parent version, both of which inquire about the child’s psychiatric symptoms. The Conduct Disorder module was administered using both youth and parent versions in the present study. Although not a focus of the current report, additional modules assessing oppositional defiant disorder, attention-deficit/hyperactivity disorder, major depression, and generalized anxiety in each child were also administered in the parent version. Both symptom counts and diagnoses were provided through computerized scoring of the DISC–IV Conduct Disorder module.
Conduct disorder diagnoses (for the past year) were made for 16 boys (2.7%) and 8 girls (1.3%) based on caregiver reports in the DISC–IV and for 9 boys (1.6%) and 2 girls (0.3%) based on child self-report. Although symptom counts for conduct disorder were significantly correlated between caregiver and youth reports (r = .31, p < .001), it is noteworthy that there was no overlap in conduct disorder diagnoses—that is, no single child reached conduct disorder criteria for both child and caregiver reports. Most likely, this pattern of results is due to the relatively young age of this sample and the fact that this is a population-based (nonclinical) sample. Although diagnosed cases according to one of the raters had elevated symptoms reported by the other rater, these individuals fell short of receiving a corresponding diagnosis from the other rater. In addition, the focus on conduct disorder behaviors during the past year (rather than lifetime prevalence used in most retrospective studies of twins) may have reduced both the prevalence of conduct disorder and the agreement among raters. Nevertheless, given the low prevalence of diagnosable conduct disorder at this age, number of conduct disorder symptoms was used rather than conduct disorder diagnosis. According to caregiver reports, 54.5% of boys and 39.2% of girls had at least one conduct disorder symptom. The corresponding figures for child reports were 47.8% of boys and 30.3% of girls.
The CAQ
This instrument was developed to assess overall, as well as various forms of, aggression. Three parallel forms of this questionnaire were used: (a) child self-report, (b) caregiver’s report of child’s behavior, and (c) teacher’s report of child’s behavior. The majority of the items were taken from Raine and Dodge’s Reactive and Proactive Aggression Questionnaire (Raine et al., 2006), including 11 reactive items (e.g., “I damage things when I am mad”; “I get mad or hit others when they tease me”) and 12 proactive items (e.g., “I threaten and bully other kids”; “I damage or break things for fun”). In addition, 5 items were added to yield relational aggression in the child and teacher versions (e.g., “I tell stories about people behind their back when I am mad at them”; “When I am mad at someone I tell my friends not to play with them”). Each of the items in the CAQ was rated on a 3-point scale (0 = never, 1 = sometimes, 2 = often), and responses were summed within each of the subtypes, for each of the 3 informants, separately. All three scales showed good internal consistency (Cronbach’s alpha ranged from .73 to .76 for child self-report, from .76 to .83 for mother ratings, and from .90 to .92 for teacher ratings).
The CPS (Lynam, 1997)
The CPS is composed of 14 subscales (based on 55 yes or no items), which consist of assessments of Glibness, Untruthfulness, Lack of Guilt, Callousness, Impulsiveness, Boredom Susceptibility, Manipulation, Poverty of Affect, Parasitic Lifestyle, Behavioral Dyscontrol, Lack of Planning, Unreliability, Failure to Accept Responsibility, and Grandiosity. Minor changes were made to the wording of some items for ease of understanding by 9- to 10-year-old children. Parallel versions of the CPS were administered to both the child and the caregiver in interview form. The two classic factors of psychopathy (Factor 1: Callous–Unemotional; Factor 2: Impulsive–Irresponsible) were derived in each of the caregiver and child self-reports, based on composites of the 14 subscales in the CPS within each rater. Both Factor 1 and Factor 2 showed reasonable internal consistency in caregiver ratings (α = .71 and .74, respectively), with somewhat lower values in child self-report (α = .63 and .61).
The CBCL (Achenbach, 1991)
The CBCL is a caregiver rating scale composed of 112 items concerning a child’s behavior within the past 12 months. Items are rated on a 3-point scale (0 = not true, 1 = sometimes or somewhat true, 2 = very true or often true) and are used to derive eight subscales: Withdrawn, Anxious/Depressed, Social Problems, Thought Problems, Attention Problems, Delinquent Behavior, Somatic Complaints, and Aggressive Behavior (Achenbach, 1991). For the purposes of the present article, however, only the Delinquent Behavior (13 items) and Aggressive Behavior (20 items) subscales were used in our analyses. The CBCL was administered during the laboratory visit to the caregivers in either survey (paper) or interview form. The CBCL was administered to the caregivers in interview form rather than in paper form if the subject’s reading comprehension skills were determined to be at or below a second-grade level as determined by the Woodcock–Johnson Reading Achievement Test (Woodcock & Johnson, 1989). Teachers were also given the parallel form of the CBCL (the Teacher Report Form) as part of the mail survey packet.
Short-Term Reliability
Thirty randomly selected families with complete data (both cotwins and their caregiver) completed the entire first-wave assessment a second time, approximately 6 months following their original laboratory visit. These retest families included exactly 50% of each gender (n = 30 boys; n = 30 girls) and were used to evaluate test–retest reliability for all measures used in this study. This sample was the basis for testing reliability of the measures across time. Test–retest correlations are presented in Table 2, separately for boys and girls, as well as for the combined sample. There was remarkable stability for these measures, although the correlations varied somewhat across rater and sex of child. Greatest stability was observed for caregiver reports, especially for ratings of boys. The lowest correlations in Table 2 are for caregiver ratings of conduct disorder symptoms (.57) and CBCL Delinquency (r = .47) in girls and for girls’ self-reported conduct disorder symptoms (r = .56). Inspecting graphical summaries of these correlations, however, revealed 1 outlier—a girl who received low ratings on these measures in the first testing and considerably higher ratings in the second. Written comments from the examiners for this family indicated that this girl had indeed experienced significant behavioral changes in the 6 months in between the two testing sessions (confirmed by both the caregiver and child examiners). Removing this case from this small sample of retest families resulted in higher retest correlations (r > .60 for all three instances). Thus, although there does appear to be considerable reliability in these measures, we must be cognizant of the fact that the potential for developmental change is possible at this age. Therefore, we suspect that these estimates of “short-term” reliability are actually conservative estimates (i.e., underestimates) of the true reliabilities.
Six-Month Test-Retest Correlations for Antisocial Behavior Measures
Statistical Analyses
General issues
Descriptive statistics, mean level comparisons, phenotypic correlations, and factor analyses were all conducted using the SPSS (Version 11.5) statistical package. Multivariate genetic analysis of the rater effects models was conducted using the structural equation modeling (SEM) program Mx (Neale, Boker, Xie, & Maes, 2003).
Missing data
Missing data for child- or caregiver reports of the different antisocial behavior measures were quite rare. For most measures, we had valid data for 1,210 to 1,219 of our total sample of 1,219 individual children. Missing data were somewhat greater for child and caregiver ratings of conduct disorder; still, we had complete child and caregiver data for more than 95% of the sample (see Table 3 for individual sample sizes for each measure). As detailed in the methods, the overall teacher response was approximately 60%; however, valid teacher-report data on the antisocial behavior measures were obtained for approximately 700 individual twins (57.4%). Of the 605 individual twin pairs, 269 pairs (44.5%) had teacher reports for both twins, and an additional 143 pairs (23.6%) had teacher reports of antisocial behavior for at least one of the two twins. Of the 269 pairs for whom we had valid teacher-report data for both twins, 111 pairs (41.4%) were in the same classroom at school and were thus rated by the same teacher informant.
Aggression, Delinquency, and Psychopathy: Means and Standard Deviations by Sex and Informant
Missing data were handled in a variety of different ways. For phenotypic analyses of mean level differences and correlations among individual subscales of ASB, a listwise deletion procedure was used, as these analyses are conducted for descriptive purposes only. For the creation of the factor-based composite scores, individuals with missing data on a given measure, within rater, were assigned a missing value for the composite scale. As missing data on individual measures were relatively rare among children and caregivers, we had valid factor scores for more than 96% of the sample (N = 1,175 for child-based factor scores, and N = 1,193 for caregiver-based factor scores). Among the 698 teachers who reported on the antisocial behavior of the children, we could create factor scores for more than 97% of them (N = 681; 55.9% of the total sample of 1,219 individuals).
One of the reasons for selecting Mx for the multivariate twin analyses is that it uses full information maximum-likelihood when fitting models to the raw data. Thus, all pairs in which at least 1 twin has nonmissing data on at least one measure can be included in the analyses, and fit functions are based on the calculation of twice the negative log-likelihood of all nonmissing observations (where an observation is defined by measure, not by individual). For the present analyses, only 1 of the 605 possible pairs did not have any usable data and was excluded from the twin analyses. Nearly all of the 605 pairs (N = 591 pairs, 97.8%) had valid composite scores for both twins based on the caregiver ratings. Over 90% (N = 559 pairs, 92.4%) of the pairs sample had both caregiver and child-report composite scores for both twins, and 42.0% of the sample (N = 254 pairs) had valid data for both twins from caregiver, child, and teacher reports. An additional 130 pairs (21.5% of the sample) had complete data from caregiver and child reports, and teacher report data for 1 member of the twin pair. Although these latter pairs could not contribute information regarding covariance across twins for teacher reports, they did provide information for the sample means and variance of the teacher reports, as well as for the within-person correlations across informants. Thus, including all 384 pairs (63.5%) for whom we had valid teacher reports for at least 1 of the 2 twins minimized potential sampling bias. Patterns of missingness did not vary significantly by sex or zygosity (results of the chi-square analyses are available upon request). For example, valid teacher report data for both twins were available from 40.8% to 48.8% of any given zygosity group. Complete pairwise data for caregiver and child reports were available for more than 92% of any given zygosity group.
Genetic models
The rater models used were based on extensions of the traditional ACE model that is typically used in behavioral genetic studies. These models use information from the observed twin variances and covariances (calculated from the raw data) to partition the overall variance into additive genetic (A), common (or shared) environmental (C), and nonshared environmental (E) influences (Neale & Cardon, 1992). In behavioral genetic models, additive genetic influences are correlated 1.0 among MZ twin pairs, as MZ twins have identical genotypes. In contrast, DZ twins share, on average, half of their segregating genes; thus, these models assume a correlation of .5 among DZ pairs. The proportion of variation that is due to genetic influences is called the heritability. Shared environmental factors include those environmental factors that serve to make individuals in a family similar to one another but that may differ across families. Thus, shared environmental influences can include such factors as SES, family structure, and shared peer influences, as well as broader contextual factors (e.g., school or neighborhood effects). In the ACE model, shared environmental influences are correlated 1.0 across twin pairs, regardless of zygosity. Nonshared environmental influences are any environmental influences that serve to make individuals dissimilar, including measurement errors (which are assumed to be random). By definition, nonshared environmental influences do not correlate across either MZ or DZ pairs.
By combining data from all three informants simultaneously in multivariate genetic models, we are able to differentiate genetic and environmental factors that influence a shared view of antisocial behavior from genetic and environmental factors that influence each informant’s own particular rating. Moreover, we can also investigate the extent to which rater effects may have biased estimates of heritability of ASB. Figure 1 shows the three multivariate models used to address this issue. All three models are variants of a common pathways model, which allowed for genetic and environmental influences on observed measures to operate through a single underlying phenotype (i.e., AC, CC, and EC; see Kendler, Heath, Martin, & Eaves, 1987; McArdle & Goldsmith, 1990, for details on common pathways models). In multiple-rater analyses, the underlying latent variable that allows for correlations across raters reflects a common, or shared, view of the child’s antisocial behavior. The genetic and environmental factors that influence this underlying shared view are further unbiased by either rater effects or measurement error, as these latter effects influence only the rater-specific views (this is discussed in more detail later). Each rater’s individual view loads on the underlying latent factor through the paths marked λ (with subscripts M, K, and T referring to caregiver [mother], child [kid], and teacher reports, respectively). Genetic and environmental influences that account for variation in the shared view of ASB are depicted through paths aC, cC, and eC (whereby the subscript C refers to influences that are common across raters). As described earlier, all additive genetic influences (A) correlate 1.0 across MZ twins and 0.5 across DZ twins, shared environmental (C) effects correlate 1.0 across twins, regardless of zygosity, and nonshared environmental influences (E) did not correlate across twins. All three models allow for informant-specific nonshared environmental influences (EM, EK, and ET), as any given measure is an imperfect estimate of the underlying “true score”; thus, informant-specific nonshared environmental effects in this model include errors of measure. In contrast, the nonshared environment that influences the common latent factor (EC) represents environmental factors that vary across twins in the same family, which are systematically associated with ASB (e.g., differential parental treatment or different peer groups).
Figure 1. (a) Rater effects model. (b) Measurement model. (c) Full common pathways model. Observed variables are represented by rectangles; latent variables are represented by circles. A = additive genetic effects; C = shared (common) environmental influences; E = nonshared environmental influences; R = rater effects; MZ = monozygotic; DZ = dizygotic; Cgvr = caregiver; Tchr = teacher. Path coefficients with a, c, e, and r correspond to the effects of these latent factors on the observed variables. Paths marked with λ represent the factor loadings on the shared view of antisocial behavior for each individual rater. Factors and corresponding path coefficients that reflect influences on the shared view of antisocial behavior are subscripted with C. The subscripts M, K, and T refer to factors and corresponding path coefficients that are specific to the caregiver (M), child (K), and teacher (T) reports, respectively. All latent A, C, E, and R factors have an assumed variance of 1.0; the variance in the factor representing the shared view has likewise been constrained to unity.
Figure 1a presents the rater effects model, which allows for additional within-informant correlation across twins for caregivers and teachers, due to the fact that the same rater is reporting on behavior for both twins. Individual twins only reported on their own behavior; therefore, it was not possible to estimate rater effects for child reports. As can be seen in Figure 1a, this model (also referred to as the correlated errors model; Simonoff et al., 1995) allows for latent variables representing rater effects to influence variation in caregiver and teacher reports (RM and RT, respectively). To the extent that ratings are influenced by the qualities of the informant, this would affect the ratings of both twins in a pair and may lead to overestimations of the twin correlations. As Figure 1a shows, the correlation for the rater effect among caregiver reports was 1.0, because all caregivers in our sample reported on the behavior of both twins. In contrast, the correlation of the rater effect for teachers could be either 1.0 or 0, depending on whether the same teacher rated both twins (a correlation of 1.0) or whether a different teacher rated each twin (a correlation of 0). By using a feature of Mx that allows for the use of definition variables as moderators of individual parameters (Neale et al., 2003), we were able to use a dummy code for each twin pair as a definition variable that represented whether the twins were in the same classroom (and thus were rated by the same teacher) to multiply the parameter for the teacher rater effect (rT) by either 1.0 (same class) or 0 (different class). If rT > 0, this would predict higher correlations among twins rated by the same teacher.
Figure 1b shows an alternative multivariate model known as the measurement model. This model, which is a restricted version of the model presented in Figure 1a, eliminates the rater effects for caregiver and teacher reports. The critical assumption of the measurement model is that the latent variable representing the shared view is the “true” representation of ASB and that all meaningful genetic and environmental influences on variation in reports of ASB are operating through the latent phenotype. Any residual variance on each rater’s individual perception of ASB that is not explained by the latent phenotype is assumed to be random measurement error that is not systematically related to characteristics of the rater and is, therefore, modeled as nonshared environment (E). Thus, the amount of variance accounted for by rater-specific E should be consistent with estimates of the reliability of each rating.
Figure 1c shows the third and final model, which is the full version of the common pathways model for multiple raters. In addition to allowing for the uncorrelated errors of measurement and rater effects (i.e., correlated errors of measurement) found in the aforementioned rater effects and measurement models, this model further allows for specific genetic and shared environmental factors to influence variation in each informant’s own ratings of the child’s ASB. For simplicity, the model is shown for 1 twin only; however, the specific A and C influences on each informant’s report of ASB correlate across twin pairs in the manner described earlier. As shown, the model allows for genetic influence on the specific viewpoints of each rater (AM, AK, and AT). The general assumption is that these genetic factors represent valid genetic variance that arises because each rater “sees” different aspects of ASB (but see the Discussion section for alternative explanations). Similarly, different rater perceptions of ASB can also be influenced by shared environmental factors (CM, CK, and CT). In this model, the potential effect of shared environmental influence on caregiver’s reports of ASB is confounded by potential rater effects, both of which would increase correlations of caregiver ratings across twins, regardless of zygosity (see Hewitt et al., 1992, for details). This is represented by the dashed lines for the RM and CM effects. Because of this confound, only one parameter can be estimated in the common pathways model, and this parameter may represent shared environment influences, a rater effect, or some combination of both. In contrast, because only some teachers rate only 1 twin per family, and others rate both twins, the shared environmental influences on teacher reports can be statistically differentiated from potential rater bias. As explained earlier, children reported only on their own behaviors; thus, the common pathways model cannot estimate rater effects for child reports.
The critical difference between this model and the models presented in Figure 1a and 1b is that this model treats differences in reports of ASB across raters as meaningful. In other words, this model assumes that there are systematic causes for disagreement among parents, teachers, and children that are not solely due to random errors of measurement and/or perceptual biases. This model would be consistent with the notion that parents, teachers, and children provide a unique perspective on the child’s behavior and that no single informant may necessarily be considered more valid or reliable than another.
Model comparisons
One of the advantages of using SEM to estimate genetic and environmental influences on variation and covariation among traits or behaviors is that SEM provides a framework for evaluating how well the theoretical model (or models) fits the observed behavior. Traditionally, two statistics have been used to compare the fit of two nested models: the likelihood-ratio test (LRT) statistic (Neale & Cardon, 1992) and the Akaike information criterion (AIC; Akaike, 1987; Medsker, Williams, & Holahan, 1994). The LRT is obtained by comparing the –2 log-likelihood (–2 LL) of a comparison model to the –2 LL of a nested (reduced) model. The LRT statistic is the difference in –2 LL between the two models, which is distributed as a chi-square statistic with degrees of freedom equal to the difference in degrees of freedom between the two models. The AIC is calculated as the LRT minus twice the difference in degrees of freedom; it indexes both goodness of fit and parsimony: The more negative the AIC, the better the balance between goodness of fit and parsimony. More recently, the Bayesian information criterion (BIC) is also being used to evaluate model fit. The BIC is similar to the AIC, except that it also adjusts for sample size (for details on the BIC and a comparison of fit statistics using simulated data, see Markon & Krueger, 2004). In this article, we present all three fit statistics, although when there is discrepancy, preference was given to the BIC (adjusted for sample size), based on the results of independent simulation studies (Markon & Krueger, 2004).
Evaluation of model fit for the multivariate analyses is done at two different levels. First, a model is fit to the data that perfectly recaptures the observed means, variances, and within- and cross-twin covariances from the three informants simultaneously. This “saturated” model provides a –2 LL statistic that can be used as the base likelihood from which the AIC and BIC statistics from each theoretical model are calculated, providing a standardized estimate of AIC and BIC values for comparison. Moreover, by comparing the fit of each of our ACE models to the fit of this saturated model using the LRT, we obtain an “absolute” estimate of how well each of our hypothesized models fits the observed data. Second, we can also calculate an LRT statistic by comparing ACE models that are “nested” within each other. We note that the measurement model (Figure 1b) is a nested submodel of the rater effects model (Figure 1a), which is itself a nested submodel of the full common pathways model (Figure 1c); therefore, LRT statistics can be calculated for each set of comparisons. Moreover, the significance of potential sex differences can also be calculated by obtaining LRT, AIC, and BIC values from a model where A, C, and E parameters are allowed to vary by sex with a model that constrains the parameters to be equal for boys and girls.
Results Sex and Informant Differences in Mean Level ASB
Descriptive statistics (means and standard deviations) for the various rating scales of aggression and delinquency are provided in Table 3, separately for caregiver, child, and teacher reports for boys and girls. These include proactive, reactive, and relational aggression, measured using the CAQ; psychopathy Factor 1 (Callous–Unemotional) and Factor 2 (Impulsive–Irresponsible) obtained on the CPS; the Aggression and Delinquency subscales from the CBCL; and conduct disorder symptom counts from the DISC–IV. Mean differences in ASB were examined between boys and girls, as well as among different informants. Significant sex differences (p < .01) emerged in the expected direction (boys > girls) for all scales except for teacher ratings of relational aggression, which showed no significant sex difference. Antisocial behavior was clearly more prevalent in boys than in girls at this age. These mean level differences were confirmed in the genetic analyses (results are available upon request); thus, means were estimated separately for males and females in all of the twin models. Although not shown in the table, it is noteworthy that diagnostic rates of disorders in this community sample are comparable to those reported in DSM–IV, for both conduct disorder (n = 25 boys, 4.2%; n = 10 girls, 1.6% received diagnoses from either youth or parent interviews) and oppositional defiant disorder (n = 70 boys, 11.9%; n = 49 girls, 8.1%; see Baker et al., 2006). Both the level and the range of ASB in this ethnically diverse community sample of twins thus appear to be comparable to those in other nontwin populations of children.
Several significant differences among informants also emerged (see Table 3). Caregivers provided significantly higher ratings than boys’ ratings of themselves for four of the five scales they had in common (reactive aggression, CPS Factors 1 and 2, and conduct disorders, but not proactive aggression). For girls, a similar pattern of higher ratings by caregivers than self-reports was also evident for several scales (proactive aggression, CPS Factor 1, and conduct disorder symptoms), although caregiver ratings of girls were lower for CPS Factor 2 and not significant for reactive aggression. Caregivers thus did not generally rate children higher or lower than children rated themselves across the board, although some rater differences were evident for both genders. Comparisons of teacher and caregiver ratings of boys also revealed significant differences for all scales except CBCL Aggression and Delinquency, although direction of difference again depended on the scale (i.e., teacher ratings lower for reactive aggression, but higher for proactive and relational aggression). The pattern of caregiver–teacher differences was similar in girls, whereby teachers again provided significantly lower ratings for reactive aggression, and all three CBCL scales, but higher ratings for proactive aggression. Teacher ratings were also significantly lower than child self-report for reactive aggression in both boys and girls, but higher for proactive aggression. Although not shown in the table, there were no differences in caregiver or child reports between children with teacher reports and children without teacher reports (results are available upon request).
The Unidimensionality of ASB in Childhood
Phenotypic correlations
We next tested whether each of the indices of antisocial behavior could be considered manifestations of a single higher order construct of externalizing behavior. We examined this through both correlational and principal-components analysis of the various ASB measures obtained through each rater. Table 4 presents the full correlation matrix (18 × 18) for boys (above the diagonal) and girls (below the diagonal). Moderate to high correlations were found among the scales of aggression and delinquency within each rater, with correlations ranging from .47 to .66 among child report measures, from .40 to .62 among caregiver report measures, and from .61 to .78 among the teacher report measures.
Phenotypic Intercorrelations Between Aggressive and Antisocial Behavior Measures for Boys (Above the Diagonal) and Girls (Below the Diagonal)
Additional comparisons of caregiver, teacher, and child reports of ASB were made by computing correlations between informants for the various scales (see Table 4). Informant agreement (indicated in boldface type in Table 4 for each measure common to two or more raters) was lowest between the child and either the caregiver or teacher (r = .17 to .29 for boys; r = .02 to .21 for girls). Agreement between caregiver and teacher ratings was somewhat higher (r = .26 to .43 for boys; r = .10 to .21 for girls) across the board. Although not shown in Table 4, correlations across raters for the composite measure of antisocial behavior (described in the next section) were also significant: r = .30 for caregiver–child agreement, r = .23 for child–teacher agreement, and r = .44 for caregiver–teacher agreement (sexes combined).
Principal-components analysis
Although all of the within-rater correlations were significant and were of moderate to high magnitude, they were not unity, which at first blush might indicate that heterogeneity of ASB may exist in these preadolescent children. The positive manifold of correlations within each rater, however, is suggestive of a single, general factor of antisocial behavior underlying the various measures. Principal-components analyses of the ASB scales within each rater confirmed that a single factor could account for much of the variance among these measures. Loadings on the first principal component within each rater are provided in Table 5, along with the percentage of variance explained among the scales in each case. All factor loadings were .70 or higher, and the general ASB factor accounted for 57.4% of the variance among the child report measures of ASB, 58.7% of variance among caregiver reports, and 77.4% of variance among teacher reports. Within each rater, scree plots clearly indicated a strong preference for a single principal component, such that only the first eigenvalue exceeded 1.0 (i.e., 3.44 for child report measures, 4.11 for caregiver ratings, and 3.87 for teacher ratings) with the second eigenvalue being clearly less than 1.0 in all three analyses (0.70, 0.72, and 0.41 for child, caregiver, and teacher ratings, respectively). It would thus appear that there is considerable overlap between the individual ASB scales, consistent with the notion of a general externalizing factor (Krueger, 2002). We therefore computed composite measures of ASB for each rater (using factor-weighted scores), and used these in the multivariate genetic models. It is noteworthy that the 6-month test–retest correlations were strong for the composite scores (r = .81 for child reports and .94 for caregiver reports) and that interrater agreement for the three composites (r = .30 between caregiver and child, r = .23 between child and teacher, and r = .44 between caregiver and teacher) was comparable to—and, in many instances, higher than—the values for each individual scale reported in Table 4. Although the parameter estimates obtained via maximum-likelihood estimation in Mx are largely robust to violations of nonnormality (Neale et al., 2003), given the slightly skewed nature of the ASB composite scores, we opted to use log-transformations to approximate normality; therefore, the biometrical model-fitting analyses were performed using the transformed scores. Parameter estimates for untransformed data were nearly identical to the results presented in this article (results are available upon request).
Factor Loadings for First Principal Component of Aggressive and Antisocial Behavior Measures Within Rater
Genetic Factor Models: Results From Multivariate Rater-Effects Models
The –2LL of the fully saturated comparison model was 7,835.34 (df = 2914). This model perfectly recaptured observed means and covariances and was therefore used to establish the adequacy of fit for each of the multivariate models shown in Figure 1. As previous analyses showed significant differences in mean level across gender (confirmed using Mx-based analyses of the composite ASB scores; results are available upon request), all subsequent multivariate models allowed for gender differences in mean levels for all three raters. The –2LL of the measurement model (see Figure 1b) was 8,161.42 (df = 3027). In comparison with the saturated model, this model fit the data very poorly by all three fit criteria (LRT = 326.08, df = 113, p < .001; AIC = 100.1, BIC = 169.8). Comparing the standard rater effects model (see Figure 1a) with the measurement model indicates that the addition of the parameters representing “correlated errors” among caregivers and teachers results in a highly significant improvement in fit (–2LL = 8,029.27, df = 3023; LRT = 132.2, df = 4, p < .001). Although the LRT statistic for the rater effects model based on a comparison with the saturated model was still highly significant (LRT = 193.93, df = 109, p < .001), both the AIC (–24.1) and the BIC (–868.2) statistics were less than zero, indicating that this model could adequately fit the observed patterns of means and variance–covariance. Nevertheless, the full common pathways model (see Figure 1c) further offered a significant improvement in fit relative to the rater effects model (–2LL = 7,965.21, df = 3013; LRT = 60.4, df = 4, p < .001). The AIC (–68.1) and the BIC (–884.1) statistics were the most negative for the common pathways model, indicating that a model that allowed for genetic and shared environmental influences on rater-specific reports of ASB in addition to the genetic and environmental influences operating through the latent variable was the best model to fit the data. In comparison with the saturated model, this model also showed a significant difference in fit by LRT criteria, indicating that the estimated variance and covariance from this model was significantly different from the observed variance and covariance, but at a much lower probability value than the other two models (LRT = 129.87, df = 99, p = .03). Finally, a model that constrained all of the parameter estimates from the common pathways model (except mean levels) to be the same for boys and girls yielded the lowest BIC statistic (–893.3), although the AIC statistic (–68.0) was nearly identical to the AIC statistic from the model that allowed these parameters to vary across gender. The –2LL for this model was 7,995.38 (df = 3028).
Standardized parameter estimates from the full common pathways model with equal effects across gender are provided in Figure 2. Estimates shown to be statistically significant at p < .05 are indicated with an asterisk (based on results of post hoc analyses; these analyses are available upon request). As shown, the common ASB factor underlying all three raters was primarily explained by genetic influences, with a heritability of .96 and no effect of shared twin environment. (In order to calculate estimates for proportions of variation, each standardized parameter estimate shown in Figure 2 is squared; i.e., h2 of shared view = .982.) Only a small proportion of variation in the underlying latent factor (.04) was explained by nonshared environmental influences (.192). Moreover, post hoc analyses indicated that these nonshared environmental influences were not statistically significant and that all variation in the latent ASB factor representing the shared view could be accounted for entirely by genetic influence (i.e., the h2 of the latent factor = 1.0). Figure 2 also demonstrates that the latent factor representing the shared viewpoint accounted for only 17.6% of the overall variation in child reports (.422) but explained approximately one third (.552 = .303) and nearly half (.672 = .449) of the variation in teacher and caregiver reports, respectively.
Figure 2. Standardized parameter estimates from full common pathways model. Paths marked with an asterisk are significantly different from zero. A = additive genetic effects; C = shared (common) environmental influences; E = nonshared environmental influences; R = rater effects. Factors influencing the underlying latent shared view of antisocial behavior are subscripted with C. The subscripts M, K, and T refer to factors that are specific to the caregiver (M), child (K), and teacher (T) reports, respectively. For Caregiver Report, rater effects (RM) and shared environmental effects (CM) cannot be statistically differentiated in this design. Thus, these influences are noted as a single path coefficient that may reflect either or both effects on variation in caregiver reports. All latent A, C, E, and R factors have an assumed variance of 1.0; the variance in the factor representing the shared view was likewise constrained to unity.
The aforementioned series of analyses indicate that models with nonrandom effects on rater-specific views of ASB provided a better fit to the data than the model, which assumed individual reports for each twin were influenced solely by random errors of measurement. As can be seen in Figure 2, correlated errors for caregiver reports, which could reflect rater effects, shared environmental influences, or both, accounted for 14.4% (.382) of the overall variation in caregiver reports and were significant. Rater effects accounted for more than one fourth of the variation in teacher reports (.532 = .281) and were significantly different from zero, as were shared environmental effects, which accounted for an additional 20.3% (.452) of the variation in teacher reports. Informant-specific shared environmental factors accounted for a nonsignificant amount of variation in child reports (.152 = .023). Finally, informant-specific genetic factors accounted for only a modest proportion of the overall variation in caregiver (.242 = .058) and teacher (.332 = .109) reports and were not significantly different from zero. In contrast, genetic factors accounted for nearly one third (.552 = .303) of the overall variation in child reports and were significant at p < .05.
Table 6 summarizes the proportions of variation in each informant’s report due to the various genetic, environmental, and rater-effects factors. In this table, we separated the influences that are common to each informant from the influences that are informant specific. A number of patterns are visible in the table. First, overall, genetic factors account for moderate amounts of variation in reports of ASB for all three raters, with heritabilities ranging from .397 (for teacher reports) to .495 (for caregiver reports). Nevertheless, an interesting pattern emerged with respect to the source of the genetic variance. For caregivers, the majority of the genetic variance (88.1%) came from the genetic influence operating through the shared view of ASB. In contrast, for child reports, only about one third of the overall genetic variation came from genetic influence operating on the shared view of ASB, and the majority of the genetic variation (64.4%) came from genetic influence on ASB that was specific to the child’s own self-rating. Teacher reports were somewhat in the middle but were more similar to caregiver ratings in that the majority of the genetic variance (72.8%) came from the genetic influence operating through the shared view of ASB. This is consistent both with the result that the child reports load less strongly on the underlying latent factor than caregiver or teacher reports and with the finding of significant informant-specific genetic influence only for child and not for caregiver or teacher reports.
Proportions of Variance Explained by Genetic and Environmental Influence: Summary of Results from the Full Common Pathways Model
The second notable pattern is that environmental influences, both shared and nonshared, influenced individual rater reports of ASB but did not play a large role in the shared view. For child reports, there was virtually no support for the effects of shared environmental factors on variation in ASB. Instead, nonshared environmental factors played a critical role in accounting for individual differences in reports of ASB and in fact accounted for a slight majority (50.6%) of the overall phenotypic variation. For caregiver reports, there were significant effects of either shared environmental influence or rater effects; however, these effects explained only a modest amount of the overall phenotypic variation (14.6%). Nonshared environmental influences accounted for roughly one third of variance in caregiver reports of ASB (36.0%). Unlike caregiver ratings, we did have the ability with teacher ratings to differentiate between shared environmental factors and rater effects. Rater effects accounted for approximately 28.1% of the variance in teacher reports, and shared environmental factors accounted for an additional 20.3% of the variance. Nonshared environmental factors showed only modest influence on teacher ratings (11.9%).
DiscussionThis article provides one of the first reports from a major longitudinal twin study of childhood aggression and antisocial behavior among a large ethnically diverse sample of twins. In this study, we focused on phenotypic and genetic analyses of antisocial behavior measures during a first wave of assessment at ages 9 to 10, when twins are on the cusp of adolescence. Instead of relying on information from one source (i.e., teacher or parent ratings of child behavior problems), we obtained ratings from 3 informants. The purpose of this article was to evaluate rater effects on the genetic and environmental influences on a shared view of antisocial behavior, using a composite measure based on a variety of types of aggressive and antisocial behavior.
In the present study we relied on composite measures of antisocial behavior created from 18 different subscales. Within each rater, subscales of reactive, proactive, and relational aggression; childhood psychopathy factors; and delinquent behavior measures (including conduct disorder symptoms) were all moderately to highly correlated with each other. These correlations (nearly uniform within each rater), as well as the results from our principal-components analyses, suggested the presence of a general antisocial or “deviance” factor underlying the various subscales provided by each rater. This general factor may be comparable to an overall externalizing factor that has been proposed by others (Achenbach & Edelbrock, 1981; Krueger et al., 2002) and reflects the wide range of behaviors exhibited by these preadolescent children. Although still somewhat negatively skewed, this general deviance factor well characterized the “shades of gray” in individual differences for antisocial behavior in this large sample and has proven useful in examining relationships with various biological (Jacobson, Zumberge, Lozano, Raine, & Baker, 2005) and social risk factors (Sanchez, Baker, & Raine, 2005) in this sample. Our continuous ASB factor may therefore reflect a wider spectrum of ASB than what is captured when relying on symptom counts (e.g., in Burt, McGue, Krueger, & Iacono, 2005) or on extreme forms of disruptive behavior, substance use, or criminal offending.
Our analyses revealed that although mean levels of ASB differed for boys and girls, the sources of individual differences in ASB were similar across gender. One of the most important findings from this study is that a shared view of antisocial behavior is strongly genetically influenced, with little or no effect of shared sibling environment. Although our analyses revealed a moderate genetic basis to individual views of antisocial behavior, with heritabilities ranging from .40 to .50 for individual composites from child, teacher, and caregiver, the estimated heritability of the underlying shared view of antisocial behavior from the common pathways model was nearly 1.0. This latent factor may reflect constellations of stable personality traits (e.g., disinhibition, lack of constraint) that may influence antisocial behavior across many contexts (Krueger et al., 2002). This highly heritable common factor representing the shared view across multiple informants could therefore prove especially useful in future investigations of specific genetic associations, or quantitative trait loci, in human aggression and antisocial behavior.
In this ethnically diverse sample of twins, heritability estimates within each rater are comparable to estimates from previous studies, which have been based primarily on Caucasian and European samples. Genetic influences for caregiver reports in our study are somewhat higher than those reported for young children in these reviews but are comparable to other recent twin studies of younger schoolchildren (Arsenault et al., 2003). The somewhat higher heritability in the present study may be due in part to our use of a general composite measure of antisocial behavior, which may be more reliable than individual subscales typically used. We have, in fact, found the pattern of genetic and environmental influences to be more variable when examining specific subscales (Raine, Baker, & Liu 2006, 2007; Ward, 2004).
Interrater agreement among caregiver, teacher, and child reports of aggression and antisocial behavior in the present study is comparable to that of other studies (Achenbach et al., 1987; Youngstrom, Loeber, & Stouthamer-Loeber, 2000); agreement is lowest between the child and either the caregiver or teacher and somewhat higher between caregiver and teacher ratings. This suggests that although there is clearly a significant degree of overlap among raters, each individual viewpoint is influenced by unique factors. Of particular importance was the identification of significant rater variance for both caregiver and teacher reports. Although we are considering these caregiver and teacher rater effects to be biases due to having the same rater report on both twins, other explanations for these “correlated errors” are possible. Specifically, among caregiver reports, it is not possible to disentangle rater effects from true effects of shared environmental influences (Hewitt et al., 1992). For example, family-level variables such as parental discipline may influence levels of antisocial behavior for twins in the same family. This shared environmental effect would also account for the correlated view in our model. Regardless of the specific source of the correlated view among caregivers, it is noteworthy that these effects accounted for a relatively modest (albeit statistically significant) proportion of variation (15%) in caregiver reports.
In contrast, for teacher reports, we were able to differentiate rater effects from true shared environmental effects, because although virtually all twins attended the same school, less than half of twin pairs were in the same classroom at school. This allowed us to disentangle shared environmental influences, which would affect the similarity of all twin pairs, regardless of classroom, from rater effects, which would only increase similarity among twins who were rated by the same teacher. In this study, rater effects accounted for more than one fourth (28.1%) of the overall variation in teacher reports. This indicates that the twins in the same classroom are rated more similarly than twins in different classrooms. Although we speculate that this is due to rater bias on the part of the teacher, it is theoretically possible that twins in the same classroom may in fact have a greater shared environment than those in separate classrooms (i.e., a direct classroom effect on behavior). To investigate this possibility, we examined post hoc whether caregiver or child ratings were also more similar if twins were in the same classroom at school, using the same dummy code for shared classroom that we used to evaluate the teacher rater effects (as described earlier). The results of these post hoc analyses indicate that being in the same classroom at school had virtually no effect on twin similarity of antisocial behavior as rated by either caregivers or the twins themselves. Being in the same classroom at school, therefore, does not lead to increased twin similarity in ASB based on either the caregiver’s or the child’s own view. Thus, our findings suggest that reports from teachers may be more heavily influenced by rater bias effects than are ratings from other reporters, leading to a spurious effect of shared environment when teacher reports are examined alone. However, in the absence of direct observational data, we cannot rule out the possibility that twins in the same classroom behave more similarly while at school. Nevertheless, if this is the case, it is important to note that these “classroom effects” are situational specific and do not affect similarity of behavior in other contexts.
In addition to these specific rater effects, shared environmental factors accounted for a significant 20% of the variation in teacher reports. These influences may reflect the effects of school context on ASB. For example, Rowe and colleagues conducted a behavioral genetic analysis within a hierarchical linear modeling framework and found that aggregate levels of parental warmth moderated both mean level of aggression as well as the overall impact of genetic and environmental influences on individual differences in aggression, with higher shared environmental influences on aggression found among twins and siblings from schools with lower average levels of parental warmth (Rowe, Almeida, & Jacobson, 1999). Their findings suggest that environmental context, measured at the school level, not only moderates mean levels of ASB but may also alter the sources of individual differences in ASB.
It is important to note that there was no indication that caregivers or teachers moderated their views of twin similarity on the basis of the twins’ zygosity. If caregivers or teachers were more likely to rate MZ twins more similarly than DZ twins, this would result in higher within-rater correlations for MZ twins than for DZ twins and would be interpreted in our model as specific genetic influences. The lack of specific genetic influence on either caregiver or teacher reports indicates that the rater effects we discovered did not upwardly bias estimates of heritability, nor can they be related to any characteristic of the child that is genetically influenced.
A different pattern emerged for the child’s own report of ASB, with at least two important findings. First, the child’s view contributes less weight to the shared view of ASB. Although the latent factor representing the shared view accounts for between 30% and 45% of the variation in caregiver reports and teacher reports, it accounts for only 17.6% of the variation in child reports. It is possible that children at this age are less reliable reporters of ASB. Such an interpretation is consistent with the fact that the 6-month test–retest correlations are somewhat lower for child reports than for caregiver reports (see Table 2) and with the higher estimate of specific nonshared environmental influence on child reports, as nonshared environmental effects include measurement error. On the other hand, the other notable finding is that child reports are the only reports that show evidence for statistically significant specific genetic influence, which may reflect genetic influence on ASB which occurs outside the radar of parent or teacher perception. If this is the case, our results may indicate that reports of ASB from children are more comprehensive and, therefore, more accurate than caregiver or teacher reports. Alternatively, the significant genetic influence on child reports may simply reflect some sort of response bias, which is correlated with genetically influenced personality traits, such as social desirability or overall honesty. Future analysis using cotwin reports of ASB may help untangle genetically influenced rater bias effects from “real” genetic influence on child reports of ASB.
Finally, our results are consistent with the idea that the greater shared environmental influence found among childhood ASB relative to adult ASB may actually be an artifact of rater bias, as studies of children often rely on caregiver or teacher reports. In this study, when young children are asked to report on their own ASB, there is no evidence for significant shared environmental influences, nor do shared environmental influences account for variation in the shared view of ASB. This is consistent with other studies that have found that much of the shared environmental variation in parent reports can be attributed to rater effects (e.g., Hewitt et al., 1992; although see Bartels et al., 2003, 2004, for contradictory results) and is consistent with the meta-analysis by Rhee and Waldman (2002), which found that shared environmental influences on ASB were higher for parental reports than for child self-reports.
The present study should be viewed in the context of several potential limitations. First, we have chosen to “lump” rather than “split” various types of ASB in these analyses. The magnitude and nature of genetic and environmental influences in ASB may very well vary across different types of ASB (e.g., Tackett, Krueger, Iacono, & McGue, 2005), a possibility we have examined in a separate article, which does suggest some distinction between aggressive–psychopathic behavior and nonaggressive delinquency, based on the underlying genetic and environmental architecture (Jacobson, Baker, & Raine, 2007). Second, the age of the sample resulted in relatively low average rates of ASB, which may limit the generalizability of these findings. We note, however, that these children and their families appeared to be a representative sample of this urban community, as their ethnic distribution and socioeconomic levels were comparable to those of the local population. Children also exhibited a wide range of behaviors, including some serious conduct problems. Third, results concerning the teacher reports should be viewed with some caution, given the somewhat modest teacher participation rate (60%). Still, teacher participation was not influenced by sex or zygosity of the twins, suggesting that our results are not an artifact of unequal participation of teachers. Moreover, we found no evidence for systematic bias due to this lower response, as caregiver and child-rated ASB did not differ for those whose teachers did and did not participate (results are available upon request). Finally, we have yet to identify the source of the strong genetic effect on the shared view of ASB found in this sample. Ongoing analyses are attempting to address this by examining the genetic covariation of the ASB measures with putative biological endophenotypes, including psychophysiological, neurocognitive, and personality measures (e.g., Baker, Isen, Bezdjian, & Raine, 2005; Jacobson et al., 2005). Additional waves of assessment are also ongoing (Wave 2, ages 11–12) with future waves planned and funded through age 17. The Wave 1 assessments of antisocial behavior described in the present article therefore provide an important basis for investigating genetic and environmental influences on the emergence of antisocial behavior in American youth throughout the course of adolescence.
Footnotes 1 Preliminary univariate analyses within each informant addressed whether models with nonadditive genetic variance fit better than models with common environmental variance (i.e., ACE vs. ADE models) and whether there was evidence for sibling interaction effects. Results indicated that the ACE model without sibling interaction effects was the best model for each informant (results are available upon request).
2 We also fit less stringent independent pathways and Cholesky models to our data. Based on Bayesian information criterion values, the common pathways model offered a better balance of goodness of fit and parsimony than either of these other less restrictive models.
3 Changing the order of the multivariate model comparisons does not change the fit statistics for the three models and therefore leads to the same conclusions. For ease of interpretation, we begin with the most restrictive model (the measurement model; see Figure 1b) and end with the least restrictive model (the common pathways model; see Figure 1c).
4 A model that further loosened the constraint that the genetic and environmental factors common to all three raters operate through a single underlying latent phenotype did not significantly improve fit relative to the more restricted common pathways model. The –2LL from this independent pathways model was 7,991.03 (df = 3024) with AIC = –64.3 and BIC = –889.0.
5 Estimates of the proportion of variance accounted for by the rater-specific effects were calculated by squaring the rater-specific paths shown in Figure 2. Estimates of genetic and environmental variance due to the common genetic and environmental factors were calculated by squaring the product of the factor loading that corresponds to the individual rater and the parameter estimate for the common genetic or environmental factor—that is, variance in child reports due to common nonshared influence is (.42 × .19)2 = .006. Estimates were calculated by Mx with parameters estimated to the fourth decimal place; thus, calculations based on paths shown in Figure 2 may vary slightly from those shown in Table 6 because of rounding error.
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Submitted: September 1, 2005 Revised: February 6, 2007 Accepted: February 9, 2007
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Source: Journal of Abnormal Psychology. Vol. 116. (2), May, 2007 pp. 219-235)
Accession Number: 2007-06673-001
Digital Object Identifier: 10.1037/0021-843X.116.2.219
Record: 22- Title:
- Identifying latent trajectories of personality disorder symptom change: Growth mixture modeling in the longitudinal study of personality disorders.
- Authors:
- Hallquist, Michael N.. Department of Psychology, State University of New York at Binghamton, Binghamton, NY, US, hallquistmn@upmc.edu
Lenzenweger, Mark F.. Department of Psychology, State University of New York at Binghamton, Binghamton, NY, US, mlenzen@binghamton.edu - Address:
- Hallquist, Michael N., Western Psychiatric Institute and Clinic, 3811 O’Hara Street, Pittsburgh, US, 15213, hallquistmn@upmc.edu
- Source:
- Journal of Abnormal Psychology, Vol 122(1), Feb, 2013. pp. 138-155.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 18
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- growth mixture modeling, longitudinal course, longitudinal study of personality disorders, personality disorder
- Abstract:
- Although previous reports have documented mean-level declines in personality disorder (PD) symptoms over time, little is known about whether personality pathology sometimes emerges among nonsymptomatic adults, or whether rates of change differ qualitatively among symptomatic persons. Our study sought to characterize heterogeneity in the longitudinal course of PD symptoms with the goal of testing for and describing latent trajectories. Participants were 250 young adults selected into two groups using a PD screening measure: those who met diagnostic criteria for a DSM–III–R PD (PPD, n = 129), and those with few PD symptoms (NoPD, n = 121). PD symptoms were assessed three times over a 4-year study using semistructured interviews. Total PD symptom counts and symptoms of each DSM–III–R PD were analyzed using growth mixture modeling. In the NoPD group, latent trajectories were characterized by stable, minor symptoms; the rapid or gradual remission of subclinical symptoms; or the emergence of symptoms of avoidant, obsessive-compulsive, or paranoid PD. In the PPD group, three latent trajectories were evident: rapid symptom remission, slow symptom decline, or a relative absence of symptoms. Rapid remission of PD symptoms was associated with fewer comorbid disorders, lower Negative Emotionality, and greater Positive Emotionality and Constraint, whereas emergent personality dysfunction was associated with comorbid PD symptoms and lower Positive Emotionality. In most cases, symptom change for one PD was associated with concomitant changes in other PDs, depressive symptoms, and anxiety. These results indicate that the longitudinal course of PD symptoms is heterogeneous, with distinct trajectories evident for both symptomatic and nonsymptomatic individuals. The prognosis of PD symptoms may be informed by an assessment of personality and comorbid psychopathology. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Disease Course; *Personality Disorders; *Symptoms
- Medical Subject Headings (MeSH):
- Adolescent; Diagnostic and Statistical Manual of Mental Disorders; Female; Humans; Longitudinal Studies; Male; Models, Psychological; Personality Development; Personality Disorders; Personality Inventory; Young Adult
- PsycINFO Classification:
- Personality Disorders (3217)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- International Personality Disorder Examination DSM–III–R Screen
Structured Clinical Interview for DSM–III–R: Nonpatient Version
State Trait Anxiety Inventory
Beck Depression Inventory DOI: 10.1037/t00741-000
NEO Personality Inventory DOI: 10.1037/t07564-000 - Grant Sponsorship:
- Sponsor: National Institute of Mental Health
Grant Number: MH045448
Recipients: Lenzenweger, Mark F.
Sponsor: National Institute of Mental Health
Grant Number: F32 MH090629
Recipients: Hallquist, Michael N. - Methodology:
- Empirical Study; Quantitative Study
- Supplemental Data:
- Tables and Figures Internet
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Dec 10, 2012; Accepted: Jul 24, 2012; Revised: Jul 10, 2012; First Submitted: Sep 21, 2011
- Release Date:
- 20121210
- Correction Date:
- 20160616
- Copyright:
- American Psychological Association. 2012
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0030060; http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0030060.supp(Supplemental)
- PMID:
- 23231459
- Accession Number:
- 2012-32960-001
- Number of Citations in Source:
- 78
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-32960-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-32960-001&site=ehost-live">Identifying latent trajectories of personality disorder symptom change: Growth mixture modeling in the longitudinal study of personality disorders.</A>
- Database:
- PsycINFO
Identifying Latent Trajectories of Personality Disorder Symptom Change: Growth Mixture Modeling in the Longitudinal Study of Personality Disorders
By: Michael N. Hallquist
Department of Psychology, State University of New York at Binghamton and Department of Psychiatry, University of Pittsburgh;
Mark F. Lenzenweger
Department of Psychology, State University of New York at Binghamton and Weill Cornell Medical College;
Acknowledgement: This research was funded in part by MH045448 from the National Institute of Mental Health to Mark F. Lenzenweger. Preparation of the manuscript was supported in part by NIMH Grant F32 MH090629 to Dr. Hallquist. We thank Armand W. Loranger for providing training and consultation on the use of the International Personality Disorder Examination (IPDE). We are grateful to Lauren Korfine for project coordination in the early phase of the study.
Although clinical thinking about personality pathology can be traced to the 19th century idea of “moral insanity” (Vaillant & Perry, 1985) and subsequent psychoanalytic studies of character pathology (Freud, 1959), the modern conception of personality disorders (PDs) originated with the introduction of the DSM–III in 1980. This nomenclature established explicit diagnostic criteria for 11 PDs putatively characterized by inflexible and maladaptive personality traits that are expressed pervasively across interpersonal situations (American Psychiatric Association, 1980). The notion that PDs are trait-like and enduring over time was largely untested at the time of the DSM–III, although contemporaneous personality research suggested a high degree of within-individual consistency over time (Costa, McCrae, & Arenberg, 1980).
To explore the stability of PD diagnoses and symptoms over time, several research groups undertook major longitudinal studies in the 1990s (Grilo, McGlashan, & Skodol, 2000; Lenzenweger, 1999; Paris, Brown, & Nowlis, 1987; Zanarini, Frankenburg, Hennen, Reich, & Silk, 2006). Accumulating evidence from these studies indicates that the mean number of symptoms for nearly all PDs declines over time and that these disorders are much less stable than previously thought (Lenzenweger, Johnson, & Willett, 2004; Skodol et al., 2005). For example, Zanarini, Frankenburg, Hennen, Reich, and Silk (2006) found that 88% of psychiatric patients with borderline PD no longer met the diagnostic threshold 10 years after diagnosis (and 39% of the sample remitted within 2 years). Furthermore, the stability of the diagnostic criteria that define certain PDs varies widely over relatively brief time intervals, suggesting that some criteria capture dysfunctional personality traits whereas others may be more sensitive to stress-related behaviors or state-dependent symptoms (McGlashan et al., 2005). Although reports from the Collaborative Longitudinal Personality Disorders Study (CLPS; Skodol et al., 2005) and the McLean Study of Adult Development (Zanarini, Frankenburg, Hennen, Reich, & Silk, 2005) have observed symptom remission for each of the PDs studied, they are potentially limited by the fact that participants were receiving psychiatric treatment at the initial study assessment and had high levels (above diagnostic threshold) of personality pathology, which raises a concern that PD symptom remission may partly reflect regression toward the mean (Campbell & Kenny, 1999).
A limitation of several longitudinal PD research reports to date (Gunderson et al., 2011; Johnson et al., 2000; Sanislow et al., 2009), including previous reports from the Longitudinal Study of Personality Disorders (LSPD; Lenzenweger, 1999), is that they have used statistical methods that characterize changes in the mean level of symptoms over time based either on group averages or individual growth curves. Such methods are insensitive to the possibility of latent subgroups mixed within the study sample whose symptoms change at different rates or who have qualitatively different symptom levels at baseline (Muthén, 2004). Thus, it remains unknown whether there are subgroups of individuals whose PD symptoms do not remit over time or affected persons whose symptoms remit especially rapidly. Even less is known about the potential development of PD symptoms among individuals who are initially asymptomatic (Cohen, Crawford, Johnson, & Kasen, 2005). Clarifying heterogeneity in the course of PDs is an important topic because persistent PD symptomatology is associated with poor treatment response (Newton-Howes, Tyrer, & Johnson, 2006) and psychosocial impairment (Gunderson et al., 2011). Thus, identifying the characteristics of individuals who experience chronic PD symptoms versus those whose symptoms remit rapidly over time may have direct implications for clinical assessment. Moreover, characterizing such heterogeneity may inform an understanding of the development and pathogenesis of personality pathology, which remains largely opaque to date.
Growth mixture modeling (GMM), a synthesis of latent growth curve modeling and finite mixture modeling, is a longitudinal data analytic approach that provides leverage on the question of whether change trajectories in a sample are homogeneous (with variation around mean parameters) or whether latent subgroups with distinct trajectories are commingled within the observed variation (Muthén & Shedden, 1999). This approach is ideally suited to parse heterogeneity in the longitudinal course of PD symptoms and has been used effectively to study the course of other forms of psychopathology (Lincoln & Takeuchi, 2010; Malone, Van Eck, Flory, & Lamis, 2010). In particular, GMM is an optimal technique for testing whether mean-level declines in PD symptoms occur universally or whether the longitudinal course of PDs is more heterogeneous than previously described.
The present study examined whether distinct latent trajectories of PD symptom change were evident in the LSPD, a multiwave prospective study designed to examine change in PD symptoms in early adulthood (Lenzenweger, 1999). Our study builds on the LSPD research corpus by focusing specifically on heterogeneity in the longitudinal trajectories of PD symptoms, whereas previous analyses of this dataset have addressed mean-level stability in the sample (Lenzenweger, 1999; Lenzenweger et al., 2004) and the associations among PDs and personality variables (e.g., Lenzenweger & Willett, 2007). Two groups of participants were observed in the LSPD: symptomatic individuals who met a diagnostic threshold for at least one DSM–III–R PD on a self-report screening instrument (PPD), and asymptomatic individuals who were drawn from a pool of subjects that did not the meet diagnostic threshold for any PD (NoPD). In the initial selection of LSPD subjects, no attempt was made to exclude participants with comorbid PDs. Thus, it is likely that symptom change at the disorder level represents a mixture of individuals with and without particular PD features, rather than a homogeneous, single-PD group. In addition, the NoPD group may have included persons at risk to develop a PD at baseline who developed personality pathology during the 4-year follow-up period.
Because the NoPD and PPD groups were sampled for the relative absence or presence, respectively, of any form of personality pathology, our primary analyses focused on identifying latent trajectories of growth in the total number of PD symptoms. This approach aligns with a large literature describing the core features of personality disorder that span diagnostic constructs (Livesley, 1998) and that are crucial in clinical decision making (Pilkonis, Hallquist, Morse, & Stepp, 2011). Furthermore, the notion of a general PD dimension is a primary component of current proposals for PD nomenclature in DSM-5 (Krueger, Skodol, Livesley, Shrout, & Huang, 2007). Separate GMMs were estimated for the NoPD and PPD groups (which varied considerably in their composition) so that the number and form of latent trajectories were not constrained by the study design. To explore heterogeneity in the course of specific PDs, we also conducted exploratory GMMs for each of the 11 DSM–III–R PDs in each group.
Personality disorders are often comorbid with each other and with Axis I psychopathology (Grilo et al., 2000; Zimmerman & Mattia, 1999), and greater comorbidity is associated with functional impairment and poor treatment response (Fournier et al., 2008; Newton-Howes et al., 2006). Moreover, personality traits may represent a common substrate that is related to many forms of psychopathology (Krueger, 2005; Krueger & Markon, 2006; Lahey, 2009). Thus, to characterize the covariation among PD symptoms, personality traits, and psychopathology, we compared PD latent trajectory classes in terms of person-specific estimates of the initial level and rate of change for all other PDs, depression, anxiety, and four major personality traits.
For the PPD group, we hypothesized that two latent trajectories would be evident for the total number of PD symptoms: (a) those whose symptoms were moderate to high at baseline and decreased little over time and (b) those with similar levels of baseline symptomatology who experienced significant remission. We further predicted that the persistent class would have greater Axis II comorbidity, anxiety, and depressive symptoms at baseline and that these comorbidities would remain higher over time than in the remitting class (Zanarini, Frankenburg, Hennen, Reich, & Silk, 2004; Zanarini, Frankenburg, Vujanovic, et al., 2004). For the NoPD group, we hypothesized that two trajectories would be observed for total PD symptoms: (a) those who exhibited minimal to subclinical symptomatology over time (consistent with the sampling strategy of the LSPD) and (b) those whose symptoms increased over time, suggesting the development of personality pathology in someone with low initial risk (Cohen et al., 2005). Analyses of individual PDs were undertaken within a context of discovery and, therefore, we did not have specific hypotheses about the number or form of latent trajectories at the disorder level.
Method Participants
Participants were 258 first-year undergraduate students from a pool of 2,000 first-year undergraduate students at Cornell University, Ithaca, NY. Subjects were drawn from all undergraduate units at Cornell, including the endowed (private) and the State University of New York units. Of the 2,000 persons randomly sampled from the incoming class, 1,658 completed the International Personality Disorder Examination DSM–III–R Screen (IPDE-S; Lenzenweger, Loranger, Korfine, & Neff, 1997). Extensive detail on the sampling procedure is given elsewhere (Lenzenweger, 2006; Lenzenweger et al., 1997). On the basis of responses to the IPDE-S, participants were divided into two groups: possible personality disorder (PPD) or no personality disorder (NoPD). Participants in the PPD group (n = 134) met the diagnostic threshold for at least one DSM–III–R PD, whereas NoPD participants (n = 124) had fewer than 10 PD features across Axis II disorders and did not meet criteria for any PD. Eight subjects did not complete the protocol because they transferred to other colleges (n = 6) or died in automobile accidents (n = 2). Thus, this study reports results from 250 subjects that completed all waves.
Complete demographic information has been reported elsewhere (Lenzenweger, 1999) and is omitted here to conserve space. The average age of the 129 participants in the PPD group was 18.85 years (SD = 0.58) and 64 were female (50%). Sixty-eight of the 121 NoPD participants were female (56%) and the average age was 18.90 (SD = 0.43). The groups did not significantly differ by age or sex composition. At study intake, 53 PPD participants met lifetime criteria for at least one Axis I disorder (41%), whereas 15 NoPD participants had at least one lifetime Axis I diagnosis (12%), and this difference was significant, χ2(1) = 24.52, p < .0001.
Participants gave voluntary informed consent and received payment of $50 at each wave. The protocol was approved by the institutional IRB of Cornell University and participants were treated in accordance with the “Ethical Principles of Psychologists and Code of Conduct” (American Psychological Association, 2010).
Measures
Personality disorder assessment
Participants completed personality disorder assessments at three time points: during the first, second, and fourth years of college. Skilled clinical interviewers administered the International Personality Disorder Examination for DSM–III–R (Loranger et al., 1994) at each measurement occasion and interrater agreement was high (Lenzenweger, 1999). Dimensional scores (i.e., number of criteria met) for the total number of DSM–III–R PD symptoms served as the primary dependent variables for the present analyses. We also explored latent trajectory models for each of the 11 DSM–III–R PDs using dimensional scores that represented sums of the individual PD criteria at each wave.
Personality assessment
At each assessment, participants completed the NEO Personality Inventory (Costa & McCrae, 1985), a well-known self-report measure of normal personality traits. Using algorithms derived from the factor analytic work of Church (1994) comparing the NEO-PI and Tellegen's constructs, we calculated scores for four major personality dimensions: Agentic Positive Emotionality, Communal Positive Emotionality, Negative Emotionality, and Constraint (for technical details, see Lenzenweger & Willett, 2007).
Proximal process assessment (early to middle childhood)
In 1991, when LSPD data collection commenced, there was no existing measure of a proximal process construct such as that hypothesized by Bronfenbrenner (Bronfenbrenner & Morris, 1998). Therefore, in consultation with Urie Bronfenbrenner, the senior investigator (MFL) developed a semistructured interview consisting of four focal questions designed to tap proximal processes in the child's relationships with important adults (e.g., parents; see Lenzenweger, 2010). Questions focused on the occurrence of regular and reciprocal involvement of an adult in facilitating the child's mastery of a task or skill, including exposure to progressively more complex information. Examples of proximal processes include teaching a child to play a musical instrument, regular reading with a child, or making plans with a child to pursue an activity of project. Assessment of these proximal process items relied upon subjects' retrospective recall, with a focus on the ages 5–12. The benefits of interviewer-based assessments for retrospective reports have been described (Brewin, Andrews, & Gotlib, 1993; Maughan & Rutter, 1997).
Axis I psychopathology assessment
Prior to the assessment of PDs at each wave, experienced clinical interviewers administered the Structured Clinical Interview for DSM–III–R: Nonpatient Version (Spitzer, Williams, & Gibbon, 1990). This well-validated semistructured interview was used to assess for DSM–III–R Axis I disorders. The presence of any lifetime Axis I disorder prior to or during the study period was the primary variable of interest. Participants were also asked whether they had sought mental health treatment at each wave, and lifetime use of treatment services was also analyzed.
In addition, participants completed the Beck Depression Inventory (BDI) and the State–Trait Anxiety Inventory—Trait Scale (STAI; Spielberger, 1983). The STAI is a well-validated 20-item self-report instrument of trait anxiety that has high internal consistency (Cronbach's alpha = .90; Ramanaiah, Franzen, & Schill, 1983). The BDI is an established 21-item self-report questionnaire that measures symptoms of depression experienced in the previous week (Beck & Steer, 1984).
Results Analytic Approach
GMMs were estimated for the total number of PD symptoms and symptoms of 11 individual PDs in each group (NoPD and PPD) using Mplus 6.12 software (Muthén & Muthén, 2010). Poisson-based models for the outcome variables were selected because PD symptoms represented counts of diagnostic criteria, which were not normally distributed, but aligned well with the Poisson distribution. Because participants varied somewhat in the timing of their follow-up assessments, individual times of observation were included in the GMMs such that growth parameters were sensitive to each person's assessment schedule. The number of latent trajectory classes was determined primarily by iteratively increasing the number of latent classes and comparing a k-class model against a model with k-1 classes using the bootstrapped likelihood ratio test (BLRT), which uses parametric bootstrap resampling to test an empirical distribution of likelihood ratio tests across bootstrapped samples. Relative to model selection criteria such as the Bayesian Information Criterion (BIC) and Akaike Information Criterion (AIC), the BLRT test is most sensitive to the number of latent classes in GMM (Nylund, Asparouhov, & Muthén, 2007) and is well-established in the finite mixture modeling literature (McLachlan & Peel, 2000). Following the recommendation of McLachlan and Peel (2000), 100 bootstrap samples were used for each BLRT computation, and the highest-class model with a significant BLRT (p ≤ .05) was selected. GMM parameter estimates describing each latent trajectory are presented in Table 1.
Initial Status and Rate of Change Estimates for Total PD Latent Trajectories Across LSPD Groups
An important point is that GMM does not inherently prefer multiclass solutions, and the empirical corroboration of a one-class GMM solution is consistent with the conclusion that a unitary mean trajectory (with normal variability around growth parameters) best characterizes the sample (cf. Bauer & Curran, 2003). Indeed, when a one-class GMM is preferred, the results are identical to the traditional latent growth curve model because the parameter estimates are no longer conditioned on latent class membership (Muthén, 2004).
In order to characterize the latent trajectories of PD symptom change, we compared classes in terms of comorbid PD symptoms and symptoms of depression and anxiety. We also compared trajectory classes on four major personality factors: Agentic Positive Emotionality, Communal Positive Emotionality, Negative Emotionality, and Constraint (Tellegen, 1985). These traits were selected because of prior research linking them to neurobehavioral systems underlying personality pathology (Depue, 2009; Depue & Lenzenweger, 2005). Mean-level differences across PD symptom latent trajectory classes were computed using the pseudoclass draw technique based on 20 pseudoclass draws from the posterior class distribution (Wang, Brown, & Bandeen-Roche, 2005). The statistical significance of mean differences in the conditional class means for each construct was evaluated using Wald tests.
To capture both initial standing and longitudinal rate of change in each of these constructs, which were measured at each wave, we conducted multilevel linear growth models using the lme4 package for R (Bates, Maechler, & Bolker, 2011; R Development Core Team, 2011). Growth models for each construct included fixed effects for group (PPD/NoPD), sex, age at study entry, and time of assessment, and random effects for subject and time. Multilevel models for the individual PDs were modeled using a Poisson distribution, whereas the other variables (personality, depression, and anxiety) were modeled as Gaussian. Individual-specific estimates of the initial level and rate of change for each construct (adjusting for group, entry age, and sex) were derived using the empirical best linear unbiased predictor (EBLUP) of the random effects (Frees & Kim, 2006). Thus, mean comparisons among classes were made both in terms of initial level and rate of change in each construct. Trajectory classes were also compared on sex, age at study entry, proximal processes, Axis I psychopathology (prior to or during the study), and mental health treatment use (prior to or during the study).
NoPD Group Results
Total PD
A three-class GMM best characterized total PD symptom change in the NoPD group according to the AICc, BIC, and BLRT (see Table 2), and there was a high degree of certainty about latent class membership, entropy = .87 (Celeux & Soromenho, 1996). The first latent class (n = 73) was characterized by low levels of PD symptoms at intake that increased slightly over time (Figure 1, left panel). The second latent class (n = 38) reported moderate to high initial levels of personality dysfunction that declined significantly over time. A third latent trajectory (n = 10) had mild to moderate PD symptomatology at intake that rapidly declined to zero by the first follow-up assessment.
Model Fit Statistics for Growth Mixture Models of PD Symptom Counts
Model Fit Statistics for Growth Mixture Models of PD Symptom Counts
Figure 1. Latent Trajectories of Total PD Symptoms. Note. Darkened circles represent the mean number of PD symptoms for individuals classified in a trajectory at that measurement occasion, where the three assessments are plotted at the median times of observation for the sample. Error bars represent the standard error of the mean.
At study baseline, symptoms of all 11 PDs, depression, and anxiety were higher in the moderate class than the low and rapid remission trajectory classes (Figure 2; Table 3). Axis I disorders were more prevalent in the moderate class (23.4%) at baseline than the low class (6.4%), χ2(1) = 4.98, p = .03, as was lifetime history of psychiatric treatment (17.2% vs. 4.3%; χ2[1] = 3.97, p = .05). The occurrence of new diagnoses or treatment utilization during the study did not differ significantly by latent class, however. Individuals in the moderate class were also approximately 4 months older, on average, than other NoPD participants (see Table 4). There was no significant difference in sex ratio across classes.
Figure 2. Mean Differences in the Initial Level and Growth of Personality Disorder Symptoms across NoPD Total PD Latent Trajectory Classes. Note. Symptom change is a rate ratio representing the expected change in the symptom count per year. Thus, a ratio of 1.0 corresponds to no average symptom change over time (shown by a horizontal black line above), ratios less than 1.0 correspond to symptom remission, and ratios greater than 1.0 indicate symptom growth. For example, a rate ratio of 1.5 would indicate that for each elapsed year, the expected number of symptoms is 1.5 times the level at the previous year.
Statistical Tests of Mean Differences in DSM-III-R PD Symptoms Across Total PD Latent Trajectory Classes
Mean Differences in Proximal Processes, Age, Personality, Depression, and Anxiety Across Total PD Latent Trajectories
Although the overall level of PD symptomatology was greater at baseline in the rapid remission class than the low-symptom class, mean comparisons for specific PDs were nonsignificant. Conversely, depressive symptoms were lowest and proximal processes were highest in the rapid remission group.
In terms of change over time, the moderate class experienced faster symptom remission for schizoid, histrionic, and obsessive-compulsive PD symptoms relative to the low class (of course, the low class had few symptoms to begin with), greater remission of anxiety symptoms, and slower symptom increases for antisocial and borderline PDs. Tempering these positive changes, dependent PD symptoms increased significantly in the moderate class over time, whereas they tended to decrease in the other two classes. Narcissistic PD symptoms remitted marginally more slowly in the moderate class than the low-symptom class, p = .07.
As detailed in Table 3, symptoms of schizotypal, narcissistic, avoidant, obsessive-compulsive, and passive-aggressive PDs declined significantly more quickly in the rapid remission class than the low-symptom class. Agentic Positive Emotionality at baseline was significantly lower in the moderate class than the low class, and Communal Positive Emotionality was marginally lower in the moderate class than the rapid remission class. Rates of change in personality variables were not associated with NoPD Total PD latent trajectory class.
Specific PDs
In the NoPD group, single-class GMMs were selected for antisocial, borderline, dependent, histrionic, narcissistic, passive-aggressive, schizoid, and schizotypal PDs. Among this set, antisocial PD symptoms increased significantly, albeit slightly, over time, whereas symptoms of histrionic, narcissistic, and schizotypal PD decreased significantly. Symptoms of borderline, dependent, passive-aggressive, and schizoid PDs were rather low in the NoPD group, on average, and exhibited no significant change over time (see Figure 3). Two-class GMMs were selected for avoidant, obsessive-compulsive, and paranoid PDs (see Table 2). Statistical tests, plots, and descriptive statistics for individual PD GMMs are included in an online supplement (e.g., Table S1), and descriptive summaries are provided here.
Figure 3. Mean Differences in the Initial Level and Growth of Personality Disorder Symptoms across PPD Total PD Latent Trajectory Classes. Note. Symptom change is a rate ratio representing the expected change in the symptom count per year.
Figure 4. Latent Growth Trajectories for Individuals in the NoPD Group. Note. Darkened circles represent the mean number of PD symptoms for individuals classified in a trajectory at that measurement occasion, where the three assessments are plotted at the median times of observation for the sample. Error bars represent the standard error of the mean.
Figure 5. Latent Growth Trajectories for Individuals in the Probable PD Group. Note. Darkened circles represent the mean number of PD symptoms for individuals classified in a trajectory at that measurement occasion, where the three assessments are plotted at the median times of observation for the sample. Error bars represent the standard error of the mean.
Avoidant PD
The majority of NoPD participants followed a low-symptom trajectory (n = 98) that had minimal symptomatology at baseline and zero avoidant symptoms at follow-up. The second latent trajectory (n = 23) had low avoidant PD symptoms at intake that increased significantly over time, approaching subclinical or clinical levels by the final follow-up. Individuals in the increasing class also had significantly higher baseline symptoms of dependent and schizoid PDs (Figure S1). In addition, individuals in the increasing avoidant PD trajectory class also experienced significantly increasing symptoms of dependent PD over time. Agentic Positive Emotionality was significantly lower in the increasing class (Table S2). Depressive symptoms, anxiety, and lifetime Axis I psychopathology did not differ across latent classes.
Obsessive-Compulsive PD
The first latent trajectory (n = 85) was characterized by few symptoms at intake and zero symptoms at the follow-up assessments. The second latent trajectory (n = 36) reported mild OCPD symptoms at intake that increased significantly over time, although average symptomatology was not high, on average, even at the final assessment. Individuals in the increasing class were more often male (64.5% vs. 43.1%; χ2[1] = 4.27, p = .04) and reported significantly higher initial levels of avoidant, paranoid, and schizoid PD symptoms relative to the low class (Figure S2). Also, dependent PD symptoms rose significantly over time in the increasing class, whereas increases in antisocial PD symptoms were greater in the low class. Remission of passive-aggressive PD symptoms was marginally slower in the increasing OCPD trajectory class, χ2(1) = 3.52, p = .06. Baseline levels of Communal Positive Emotionality were significantly lower in the increasing OCPD class (Table S2).
Paranoid PD
Whereas paranoid PD symptoms tended to decrease to zero in the majority of NoPD participants (n = 97), a second latent subgroup (n = 24) experienced significant increases in symptoms, although overall symptom levels remained subclinical throughout the study. Lifetime history of psychiatric treatment was greater in the increasing class (20.1% vs. 4.0%; χ2[1] = 4.39, p = .04), but Axis I diagnosis at baseline, depressive symptoms, and anxiety did not differ by latent class. Membership in the increasing latent class was marginally associated with greater symptoms of narcissistic PD at baseline, χ2(1) = 2.70, p = .10, but no other cross-PD associations approached statistical significance (Figure S3). Although the latent class differences for individual Cluster B PDs were nonsignificant, the total number of Cluster B symptoms at baseline was greater in the increasing class, χ2(1) = 3.93, p = .05. The classes were not significantly different on any personality variables.
PPD Group Results
Total PD
A three-class GMM best described the course of total PD symptoms in the PPD group according to AICc, BIC, and BLRT (see Table 2), entropy = 0.94. The majority of participants (n = 109) were classified into a trajectory characterized by moderate to high PD symptoms that declined significantly over the follow-up period, particularly between baseline and the 1-year follow-up assessment (see Figure 1, right panel). A second latent class (n = 11) had few PD symptoms at intake, but experienced mild symptom increases over the course of the study. The third trajectory class (n = 9) had high levels of PD symptoms at baseline, but experienced rapid symptom remission, approaching zero symptoms at the 1-year follow-up.
Individuals in the high-symptom class were more often male (54.4% vs. 21.6%, χ2[2] = 6.79, p = .03) and had a greater lifetime prevalence of Axis I disorders (46.0% vs. 12.5%, χ2[2] = 9.07, p = .01) relative to the other two groups. Relative to the rapid remission class, more high-symptom class members had received psychiatric treatment in the past (17.2% vs. .8%, χ2[1] = 8.37, p = .004) and there was also a greater incidence of new Axis I disorders in the high-symptom class (22.9% vs. .7%, χ2[1] = 16.28, p < .0001). Proximal processes were significantly higher in the low-symptom class than the high-symptom class (see Table 4). In terms of specific PD symptoms at intake, the high-symptom class had greater initial levels of all 11 PDs than the low-symptom class (see Figure 4). In addition, the high-symptom class reported significantly higher baseline levels of antisocial, borderline, dependent, and schizoid PD symptoms than the rapid remission class. Relative to the low-symptom class, the rapid remission class had higher levels of histrionic and narcissistic PD symptoms at baseline, and there were positive trends for avoidant, dependent, obsessive-compulsive, passive-aggressive, and schizotypal PDs.
Figure 4. Latent Growth Trajectories for Individuals in the NoPD Group. Note. Darkened circles represent the mean number of PD symptoms for individuals classified in a trajectory at that measurement occasion, where the three assessments are plotted at the median times of observation for the sample. Error bars represent the standard error of the mean.
In the rapid remission class, the rate of symptom decline exceeded the other trajectory classes for dependent, narcissistic, obsessive-compulsive, paranoid, passive-aggressive, and schizotypal PDs (see Table 3). Avoidant PD symptoms also decreased more quickly in the rapid remission class than the high-symptom class. Overall, the rates of change in PD symptoms were similar in the low-symptom and high-symptom classes (see Figure 4). However, antisocial PD symptoms increased more quickly and borderline PD symptoms decreased more slowly in the rapid remission and low-symptom classes relative to the high-symptom class. The slight symptom increases observed in the low-symptom class appear to have been driven by greater increases in Antisocial PD symptoms as well as relatively little change, on average, in borderline and paranoid PD symptoms.
Negative Emotionality at baseline was significantly higher in the high-symptom class than the rapid-remission class. Notably, however, Negative Emotionality decreased more rapidly over time in the high-symptom and low-symptom classes than the rapid-remission class. There was a statistical trend toward lower levels of Communal Positive Emotionality in the high-symptom class relative to the other classes. Also, anxiety and depression were highest in the high-symptom class at baseline, but anxiety also decreased most rapidly in this class.
Specific PDs
In the PPD group, a single-class GMM best described schizoid PD symptoms, but multiple latent trajectories were evident for the other 10 PDs (see Figure 5). Schizoid PD symptoms were low and stable in the PPD group.
Figure 5. Latent Growth Trajectories for Individuals in the Probable PD Group. Note. Darkened circles represent the mean number of PD symptoms for individuals classified in a trajectory at that measurement occasion, where the three assessments are plotted at the median times of observation for the sample. Error bars represent the standard error of the mean.
Antisocial PD
A latent trajectory class that included 79 PPD participants was characterized by subclinical-to-clinical levels of antisocial features that increased slightly, but significantly, over time. The second trajectory class (n = 50) was associated with few antisocial symptoms at intake and zero symptoms at follow-up. A greater proportion of males was represented in the increasing class (71.6% vs. 41.9%; χ2[1] = 7.24, p = .007). Baseline symptoms of borderline, paranoid, and schizotypal PDs were significantly higher in the increasing class (Figure S4). Whereas narcissistic PD symptoms declined somewhat more slowly in the increasing class, χ2[1] = 3.44, p = .06, borderline PD symptoms declined significantly faster in the increasing class. Constraint at baseline was significantly lower in the increasing antisocial PD class (Table S3).
Avoidant PD
Two latent trajectories for avoidant PD symptoms were evident in the PPD group. Seventy-six individuals had few, if any, symptoms at intake and zero symptoms at follow-up assessments. A second latent trajectory (n = 53) was characterized by subclinical-to-clinical avoidant PD symptoms (13 individuals had four or more symptoms, the threshold for diagnosis) that remained relatively stable over time. The subclinical group had a greater proportion of individuals with a lifetime Axis I disorder at baseline (51.6% vs. 27.7%, respectively; χ2(1) = 6.23, p = .01), but treatment utilization and incidence of Axis I disorders did not differ by class. Baseline levels of dependent, histrionic, passive-aggressive, schizoid, and schizotypal PDs were significantly higher in the subclinical trajectory than the low-symptom trajectory (Figure S5). Also, symptoms of dependent and obsessive-compulsive PDs remitted more slowly in the subclinical group. Negative Emotionality was significantly higher at baseline in the subclinical group, as were symptoms of depression.
Borderline PD
A four-class GMM best fit the longitudinal course of borderline PD symptoms in the PPD group. The first latent class (n = 57) had zero or one BPD symptoms throughout the study. The second latent class (n = 39) reported mild to subclinical symptoms that did not change over time. A third trajectory (n = 17) was characterized by clinical BPD symptoms at intake that remitted significantly, in most cases, to subclinical symptoms by the final follow-up. Finally, a fourth trajectory (n = 16) had subclinical-to-clinical symptoms at intake that remitted rapidly, with all individuals in this class having two or fewer symptoms at the final assessment.
Lifetime Axis I disorders at intake were significantly more prevalent in the high-symptom and rapid remission classes than the mild and minimal classes (72.7%, 62.9%, 37.7%, and 25.9%, respectively; χ2[3] = 14.37, p = .002). Interestingly, 40.3% of individuals in the mild BPD symptom trajectory developed new Axis I disorders during the study—significantly more than other trajectory classes (minimal = 15.0%, high = 16.5%, rapid remission = 7.2%; χ2[3] = 10.24, p = .02)—raising the possibility that BPD symptoms at baseline may have been precursors of subsequent psychopathology. Trajectory classes did not differ by sex, age, or treatment utilization.
In the high-symptom and rapid remission classes, baseline symptoms of antisocial, paranoid, and schizotypal PDs were significantly higher than the minimal symptom class. The high-symptom class was further distinguished by elevated symptoms of dependent, histrionic, narcissistic, and passive-aggressive PDs at baseline relative to the other three trajectories. The mild symptom class reported higher initial levels of antisocial PD than the minimal class (Figure S6).
In addition to remitting more quickly on borderline PD symptoms, individuals in the rapid remission class exceeded those in the high-symptom class in the rate of remission for avoidant, narcissistic, passive-aggressive, and schizotypal PD symptoms, and they also had slower growth in antisocial PD symptoms than the high-symptom class. Further, in the high-symptom class, symptoms of avoidant, dependent, narcissistic, and passive-aggressive PDs declined more slowly than in the mild and minimal trajectory classes. Symptoms of Paranoid PD declined more slowly in the mild class relative to the rapid remission class.
As detailed in Table S3, Constraint at baseline was significantly lower in the mild, rapid remission, and high symptom classes than the minimal symptom class. Negative Emotionality at baseline was highest in the high-symptom class, exceeding all other classes, whereas the rapid remission and mild symptom classes had higher levels of Negative Emotionality than the minimal class. Negative Emotionality decreased significantly more quickly over time in the rapid remission class than the mild and minimal symptom classes. Anxiety at baseline was highest in the high symptom class, followed by the mild symptom class, with the rapid remission and minimal classes having the lowest levels of anxiety. That said, anxiety also decreased more rapidly in the high symptom class than the minimal class. Baseline depression was highest in the high symptom class, followed by the rapid remission class, with the mild and minimal classes having the lowest levels. Depressive symptoms decreased significantly more quickly in the rapid remission class than the mild and minimal classes.
Dependent PD
Two latent trajectories were evident for Dependent PD symptoms: the first (n = 73) had few features at baseline and zero features at follow-up assessments, whereas the second trajectory (n = 56) had mild to moderate symptoms at baseline that decreased marginally over time (p = .06). Lifetime history of Axis I was significantly higher in the moderate class (52.7% vs. 24.8%; χ2[1] = 8.18, p = .004). Symptoms of avoidant, borderline, histrionic, narcissistic, and passive-aggressive PDs were significantly higher at baseline in the moderate class than in the minimal class (Figure S7). In addition to having more persistent dependent PD symptoms, the moderate trajectory was associated with slower declines in avoidant and obsessive-compulsive PD symptoms. Negative Emotionality, anxiety, and depressive symptoms were significantly higher in the moderate trajectory at baseline, but the rates of change in these constructs did not differ by trajectory class (Table S3).
Histrionic PD
Three latent trajectories characterized the level and rate of change in histrionic PD symptoms. The first class (n = 76) reported moderate symptoms of histrionic PD that decreased significantly over time. The second class (n = 45) experienced zero histrionic PD symptoms throughout the study. The third class (n = 8) reported moderate to severe histrionic PD symptoms at baseline but had zero symptoms at each follow-up.
Lifetime history of Axis I psychopathology was significantly higher at baseline in the moderate class than the zero class (51.7% vs. 19.0%, respectively; χ2[1] = 10.89, p = .001). Relative to the zero class, the moderate class also had significantly higher baseline levels of avoidant, borderline, dependent, narcissistic, obsessive-compulsive, paranoid, and passive-aggressive PDs (Figure S8). At baseline, the rapid remission class had higher levels of narcissistic PD than the zero class and lower levels of borderline PD than the moderate class. Symptoms of narcissistic and passive-aggressive PDs remitted more quickly in the rapid remission class than the moderate class. Symptoms of dependent PD were more persistent in the moderate class than the zero class. Negative Emotionality and depressive symptoms were significantly higher at baseline in the moderate class relative to the zero class.
Narcissistic PD
Two latent trajectory classes were evident for narcissistic PD: the first class (n = 71) reported few symptoms at baseline and zero symptoms at follow-up assessments. The second class (n = 58) reported subclinical to clinical levels of narcissistic PD, and these symptoms declined significantly over time. Symptoms of antisocial, borderline, histrionic, paranoid, and passive-aggressive PDs were significantly higher at baseline in the moderate class than the minimal class. Over the course of the study, symptoms of passive-aggressive and obsessive-compulsive PDs declined more slowly in the moderate class (Figure S9). There were no significant differences between trajectory classes in terms of personality, anxiety, or depressive symptoms.
Obsessive-compulsive PD
Three latent trajectories were identified that described change in OCPD symptoms over time. The first class (n = 54) reported moderate levels of OCPD at baseline that increased slightly, but significantly, over time. The second latent class (n = 52) had few OCPD symptoms at baseline and zero symptoms at follow-up. The third class (n = 23) reported clinical levels of OCPD at baseline that remitted rapidly approaching zero by the final assessment. The moderate class had significantly higher baseline levels of avoidant, dependent, narcissistic, paranoid, passive-aggressive, schizoid, and schizotypal PDs relative to the minimal class (Figure S10). Although baseline PD levels were often similar in the rapid remission and moderate classes, most of the statistical tests of class means were nonsignificant. Avoidant PD symptoms, however, were significantly higher in the rapid remission class than the minimal class.
Over time, slower declines were evident in the moderate class for dependent, narcissistic, paranoid, passive-aggressive, and schizotypal PD symptoms relative to the minimal class. In addition to remitting more quickly on OCPD symptoms, the rapid remission class also declined more quickly than the moderate class on symptoms of avoidant and narcissistic PDs. Communal Positive Emotionality was lower at baseline in the moderate class than in the minimal class, whereas anxiety and depression were highest in the moderate class (Table S3). Further, whereas constraint increased more quickly in the moderate class than the minimal class, growth in Communal Positive Emotionality over time was smallest in the moderate class.
Paranoid PD
A two-class GMM best fit paranoid PD symptoms in the PPD group. The first latent class (n = 74) reported few symptoms at baseline and zero symptoms at follow-up. The second latent class (n = 55) experienced mild to moderate symptoms at baseline that were stable over time. The moderate class had higher baseline symptoms of antisocial, avoidant, borderline, dependent, histrionic, narcissistic, and schizotypal PDs (Figure S11). Symptoms of avoidant, dependent, narcissistic, obsessive-compulsive, and passive-aggressive PDs declined more quickly in the minimal symptom class relative to the moderate class. Constraint was marginally lower in the moderate class at baseline, whereas initial anxiety and depression were significantly higher in this class.
Passive-aggressive PD
Three latent trajectories characterized symptoms of passive-aggressive PD. The first trajectory (n = 68) reported minimal symptomatology throughout the study. Individuals in the second trajectory (n = 35) reported subclinical levels of passive-aggressive PD at baseline that declined rapidly over time, reaching zero by the final follow-up. The third class (n = 26) reported subclinical to clinical levels of passive-aggressive PD at baseline that were stable over time. Relative to the minimal and rapid remission classes, the moderate class reported higher baseline levels of antisocial, borderline, histrionic, narcissistic, and paranoid PDs (Figure S12). Obsessive-compulsive PD features were also higher in the moderate class than the minimal class. Avoidant, dependent, and narcissistic symptoms remitted more slowly in the moderate class than the minimal class. Constraint and Communal Positive Emotionality were significantly lower at baseline in the moderate class than in the minimal and rapid remission classes, whereas baseline depression and anxiety were significantly higher in the moderate class.
Schizotypal PD
Two latent classes characterized schizotypal PD symptom trajectories. Seventy-four individuals experienced minimal schizotypal PD symptoms at intake that declined significantly over time, with all individuals reporting zero symptoms the two follow-up assessments. The second trajectory class (n = 55) reported subclinical to clinical levels of schizotypal PD at baseline and these symptoms declined significantly over time. There were marginally more females in the minimal symptom class than the moderate class (60.3% vs. 42.2%, respectively; χ2[1] = 3.08, p = .08). Symptoms of antisocial, avoidant, borderline, narcissistic, obsessive-compulsive, and schizoid PDs were significantly higher at baseline in the moderate class than the minimal class, but the rate of change in comorbid PD symptoms did not differ by latent class (Figure S13). Communal Positive Emotionality was significantly lower in the moderate schizotypal PD trajectory class, whereas anxiety was marginally higher (Table S3).
DiscussionRecent empirical findings from prospective longitudinal studies challenge the notion that PDs have a chronic course, with multiple studies demonstrating mean-level declines in PD symptoms over time (Johnson et al., 2000; Lenzenweger et al., 2004; Sanislow et al., 2009; Shea et al., 2002; Zanarini et al., 2006). Consistent with clinical observations (e.g., Stone, 1990), however, the expression of personality pathology over time differs across individuals, and there may be considerable variability in the longitudinal trajectories that people follow. In this study, we sought to characterize directly heterogeneity in the longitudinal course of PDs using growth mixture modeling, with the goal of identifying potentially distinctive trajectories over a 4-year observational longitudinal study of young adults. Our findings build upon previous longitudinal reports from the LSPD data (e.g., Lenzenweger et al., 2004) through the use of latent trajectory analyses and the richer characterization of longitudinal covariation among personality and comorbid Axis I and II symptoms. Our results corroborated the existence of multiple latent trajectories for the overall level of personality dysfunction, both for the symptomatic (PPD) and asymptomatic (NoPD) groups comprising the LSPD sample. This is the first study of personality disorders to characterize longitudinal heterogeneity in terms of qualitatively distinct symptom trajectories, and the results have important theoretical and clinical implications.
In the NoPD group, the majority of individuals followed trajectories characterized by minimal PD symptomatology at baseline (both in terms of the total number of PD symptoms and symptoms of specific disorders) that was relatively stable over the follow-up period. This result suggests that most individuals who have little or no personality pathology in early adulthood are unlikely to develop subsequent symptoms. This finding is novel insofar as previous research in this area has not probed specifically for the development of personality pathology in initially asymptomatic individuals. Approximately 30% of the NoPD group, however, experienced subclinical levels of overall personality dysfunction, which remitted significantly, but not completely, over the follow-up period. This finding is consistent with prior reports from the LSPD (Lenzenweger et al., 2004) and other longitudinal studies (Grilo et al., 2004) that initially symptomatic individuals often show symptom remission even over brief intervals. Finally, a small subset of NoPD participants experienced subclinical personality dysfunction at baseline that remitted entirely at the follow-up assessments. Given that study participants were college freshman at baseline, it is possible that the rapid remission of PD symptoms in some individuals may reflect initial turmoil upon entering college followed by adjustment and recovery.
NoPD individuals following the moderate PD symptom trajectory tended to have lower levels of Communal and Agentic Positive Emotionality at baseline, higher baseline anxiety and depressive symptoms, greater lifetime prevalence of Axis I psychopathology, and greater lifetime utilization of mental health treatment. By contrast, rapid remission of subclinical symptoms was associated with low levels of depression and higher proximal processes. The latter suggests that proximal processes may buffer the risk for persistent personality dysfunction and support the development of social affiliation (Lenzenweger, 2010).
The emergence of separate minimal- and moderate-symptom trajectories in the NoPD group is interesting because it suggests a potential dichotomy between individuals who have virtually no personality dysfunction and those whose symptoms, although not reaching the level of clinical diagnosis, are moderately persistent over time and are associated with Axis I psychopathology and low positive emotionality. NoPD individuals were sampled to have 10 or fewer PD symptoms at baseline, yet our results are inconsistent with the notion that PD symptomatology in a low-risk group varies dimensionally. This finding suggests the possibility that studies that have used a dimensional cutoff to identify individuals low in psychopathology (e.g., Bagge et al., 2004) may have included a mixture of individuals—some with subclinical psychopathology and some with minimal symptomatology. Also, the strong link between subclinical personality dysfunction and Axis I psychopathology, both lifetime and at study baseline, in the NoPD moderate-symptom trajectory raises questions about the boundaries between PDs and clinical syndromes (Krueger, 2005). For example, we found that the remission of PD symptoms in the NoPD moderate-symptom trajectory covaried with the remission of anxiety symptoms (cf. Tyrer, Seivewright, Ferguson, & Tyrer, 1992).
Although increasing symptoms of personality dysfunction were not evident in the NoPD group when symptoms were considered in aggregate, we identified latent trajectories for avoidant, obsessive-compulsive, and paranoid PDs that were characterized by greater symptomatology over time, consistent with our hypothesis that personality dysfunction develops in some young adults who were previously nonsymptomatic. In most cases, symptom severity remained below diagnostic thresholds, but six individuals (5% of the NoPD sample) exhibited increasing symptoms over the follow-up period that resulted in new PD diagnoses at the final assessment (four avoidant PD, one OCPD, and one paranoid PD). NoPD participants characterized by increasing symptom trajectories tended to have greater Axis II comorbidity at baseline (especially avoidant, dependent, and schizoid PDs) and to exhibit increasing symptoms of dependent PD over time. Communal and Agentic Positive Emotionality were also lower for those in the increasing avoidant and obsessive-compulsive symptom trajectories. These findings are novel and illustrate the importance of studying low-risk individuals using methods such as GMM to detect meaningful symptom increases over time. Furthermore, the finding of de novo personality dysfunction in young adults raises questions about the developmental psychopathology and etiology of PDs. Developmental research has previously implicated adolescence as a key risk period for the onset of serious personality dysfunction (Johnson et al., 2000), yet our findings suggest that risk for PDs continues into early adulthood in some cases.
In the PPD group, three latent trajectories for total PD symptomatology were identified: Many individuals experienced considerable remission of moderate to severe symptomatology, some individuals experienced rapid remission, and a small subset reported few symptoms throughout the study. Symptom remission in the moderate trajectory was especially rapid between the baseline and 1-year follow-up assessments, which is consistent with previous reports on the LSPD sample (Lenzenweger, 1999; Lenzenweger et al., 2004), as well as a growing literature on mean-level declines in personality pathology in adulthood, particularly among symptomatic individuals and psychiatric patients (McGlashan et al., 2005; Sanislow et al., 2009; Zanarini et al., 2006). GMMs for individual PD symptoms in the PPD group often identified a latent trajectory characterized by moderate symptoms that declined somewhat or were persistent over time. Incidence and lifetime prevalence and of Axis I psychopathology were higher in the moderate trajectory classes, as was lifetime mental health treatment utilization. A key finding from our study was that slower remission of PD symptoms, whether for total symptom counts or for individual PDs, was closely linked with comorbid Axis II psychopathology. More specifically, baseline levels of PD symptoms were highly overlapping, consistent with previous reports on the poor discriminant validity of PDs (Sanislow et al., 2009; Zanarini et al., 2004; Zimmerman, Rothschild, & Chelminski, 2005). Furthermore, the rates of remission across PDs were often coupled such that slower declines for symptoms of one PD were accompanied by slow declines in comorbid PDs.
A fraction of PPD participants experienced rapid remission of total PD symptoms, dropping 15 or more symptoms within a single year. Exploratory GMMs of individuals PDs corroborated the existence of rapid remission trajectories for borderline, histrionic, obsessive-compulsive, and passive-aggressive PDs. Rapid remission of specific PD symptoms was associated with concomitant declines in comorbid PD symptomatology, higher proximal processes in childhood, lower Negative Emotionality at baseline, higher Positive Emotionality, and higher Constraint. For borderline PD symptoms, rapid remission was also linked with decreasing Negative Emotionality over time, suggesting meaningful temporal links between personality and PDs. This topic has explored by Warner et al. (2004) in the CLPS dataset, who found that changes in personality traits often preceded declines in PD symptoms. A previous report from our group (Lenzenweger & Willett, 2007) also described links between personality and PDs, finding that the initial level of Negative Emotionality, Positive Emotionality, and Constraint were often predictive of PD symptom trajectories over time.
Despite reporting a number of PD symptoms on the self-report screening questionnaire, a fraction of PPD participants were best classified by a latent trajectory with few symptoms upon clinical interview at each assessment. This latent trajectory was unexpected but illustrates the potential for false positives when self-report screening measures are used, and it reinforces the compelling literature describing discrepancies among sources of information about personality dysfunction (e.g., Oltmanns & Turkheimer, 2006).
The consistent identification of remission trajectory classes across NoPD and PPD groups suggests that transient personality pathology probably occurs in a subset of the population and deserves further study. As articulated above, numerous features distinguished rapid remission trajectories from trajectories characterized by slower symptom declines, including fewer comorbid PD symptoms, higher Communal Positive Emotionality, higher Constraint, lower Negative Emotionality, and lower rates of Axis I psychopathology. Nevertheless, because transient personality dysfunction was evident at the baseline assessment in our data, it is difficult to know the precursors of such trajectories. Our findings suggest that a more salubrious configuration of baseline personality traits was associated with a rapid remission latent trajectory for specific PD symptoms, which comports with previous studies (Lenzenweger & Willett, 2007; Warner et al., 2004).
The approach and findings of this report extend beyond previous analyses of the LSPD and other longitudinal studies of PDs in two major ways. First, we have used GMM to test for heterogeneity in the longitudinal course of PDs, and our results corroborated the existence of distinct symptom trajectories for overall personality dysfunction and for many specific PDs. Initial findings from the LSPD (Lenzenweger et al., 2004) and other major studies of PDs (e.g., Gunderson et al., 2011) have used methods that assume that the longitudinal course of PDs can be adequately summarized by a single mean trajectory (allowing for normal variation around the mean). Such methods may have averaged over clinically meaningful variability. In the PPD group, for example, a traditional growth curve model would have averaged together the low-symptom/false positive and high-symptom trajectories, potentially providing an overly optimistic view of symptom remission. Furthermore, the rapid remission trajectory, which was markedly different on various measures of Axis I psychopathology, PD symptomatology, and personality traits, would have been missed altogether, resulting in the combination of two groups with different prognoses.
Second, we have analyzed PD symptom data using Poisson-based GMMs, rather than treating PD symptom data as Gaussian. Although this is a technical innovation, it has important practical significance. Poisson-based growth models represent change over time in terms of the natural logarithm of PD symptomatology, such that nonlinear growth curves can be accommodated. As is evident in Figures 1, 3, and 5 the longitudinal course of PDs is linear in some cases and quite nonlinear in others. Thus, the assumption of linear change implicit in previous reports, including those from our group (Lenzenweger et al., 2004), may not be supported by the data, and substantive conclusions about the course of PD symptoms may be considerably different if nonlinear models of change are considered. For example, PD symptoms in the LSPD tended to change most between the first and second assessments (cf. Lenzenweger, 1999), whereas changes at the final follow-up assessment were subtler. The Poisson-based growth model captures this meaningful nonlinearity (as might be evident in a more traditional ANOVA approach) and retains the strengths of a growth modeling framework (cf. Lenzenweger et al., 2004). Poisson models are also better suited to count data that have low means and/or many zero values, as is common with PD symptom data, and Gaussian models of such data may fail to capture the relationships among PDs, personality traits, and other forms of psychopathology (Wright & Lenzenweger, in press).
Altogether, the present study revealed that there is considerable heterogeneity in the longitudinal course of PD symptoms, both for asymptomatic and symptomatic individuals. This work provides an initial demonstration that traditional growth modeling techniques may tend to overemphasize commonalities in the course of PDs (i.e., the mean growth trajectory) while missing important latent trajectories mixed within the data. That said, even among symptomatic individuals, only antisocial, obsessive-compulsive, and passive-aggressive PDs included a latent trajectory with stable, persistent symptoms, suggesting that previous research on mean-level declines in PD symptoms presents a reasonably accurate picture of the modal course of personality pathology. Clinically, our findings underscore the importance of assessing for comorbid Axis I and II disorders when diagnosing PDs (Grilo et al., 2000; Loranger et al., 1991; Morey et al., 2010; Zanarini, Frankenburg, Vujanovic, et al., 2004; Zimmerman et al., 2005) and also point to the incremental utility of considering personality dimensions when formulating treatment plans (Harkness & Lilienfeld, 1997).
Our study had several limitations. First, because the LSPD sampled for overall personality dysfunction, individual PD GMM results should be interpreted with caution because symptoms of some clinical disorders (e.g., schizoid) were low. Thus, our finding that the course of some PDs was best characterized by a single trajectory should not be interpreted as evidence that some PDs are relatively homogeneous over time, whereas others show marked discontinuities. Neither should the number or form of latent trajectories in our study be seen as an authoritative description of change in PD symptoms. GMM is sensitive to the composition of the sample and is potentially vulnerable to overextraction of latent trajectories when model assumptions are violated (Bauer & Curran, 2003). We also note that our characterization of the links between personality and PD symptomatology focused only on major traits, and a finer analysis of traits may reveal incremental information about covariation among these constructs (Widiger & Simonsen, 2005).
Prior research has also documented that PD diagnostic criteria have different levels of stability over time, with some criteria likely reflecting trait-like characteristics, whereas others may potentially reflect stress-related behaviors (Gunderson et al., 2003; McGlashan et al., 2005). Thus, the use of summed symptom counts in the present study limited our ability to test for criterion-level differences in the longitudinal course of PD symptoms. Growth mixture modeling can accommodate more complex measurement models that would be sensitive to differential criterion stability, but a much larger sample would be needed to estimate the high number of parameters required for such models. There is a possibility that the remission of PD symptoms in some individuals might reflect a retest artifact whereby study participants are more likely to deny symptomatology at follow-up, perhaps because of a desire to shorten the interview or due to boredom. Although this issue has not been studied closely in the PDs literature, there is little evidence that retest artifacts are likely to account for the remission of PD symptomatology, particularly over longer retest intervals (Loranger et al., 1991; Samuel et al., 2011; Zimmerman, 1994).
The LSPD subjects are now approaching age 40 and will be assessed again in the fourth wave of this ongoing project, which will allow for a 20-year follow-up assessment that could be studied using the GMM approach articulated here. Future research should investigate more closely the emergence of personality pathology, especially avoidant, obsessive-compulsive, and paranoid PDs, in adulthood. Our findings suggest that lower levels of Positive Emotionality, existing subclinical symptoms of PDs, and increasing symptoms of dependent PD may be associated with risk for the development of clinically significant personality dysfunction in early adulthood, but this novel finding needs to be validated in an independent sample. Also intriguing is that personality dysfunction may be transient in some individuals, and prospective longitudinal studies of low-symptom individuals may help to identify the precursors of individuals whose PD symptoms are rather brief. A possibility suggested by our data is that an adaptive configuration of personality traits (e.g., low Negative Emotionality and high Constraint) may help to guard against the long-term persistence of PD symptoms. Consistent with the growing literature on the associations between normative and abnormal personality (Widiger & Simonsen, 2005) and the common neurobehavioral systems that may give rise to personality and PDs (Depue & Lenzenweger, 2005), we hope that future research may help to uncover the links between transient personality pathology and normative personality traits. Altogether, our results demonstrate the power of growth mixture modeling to uncover qualitatively distinct longitudinal trajectories of PD symptoms that are differentially associated with Axis I psychopathology, comorbid PD symptomatology, and personality traits. We hope that our study stimulates further research on the longitudinal heterogeneity of PDs and that theories of personality and psychopathology explore more specifically the pathogenesis of transient, emergent, and persistent personality dysfunction, as well as the mediators of PD symptom remission.
Footnotes 1 Although Total PD symptoms increased slightly in the low-symptom trajectory, symptom levels remained low, and the increase may be reflective of regression toward the mean. We thank a reviewer for suggesting this interpretation.
2 More specifically, Poisson models are linear with respect to the link function, which is the natural logarithm of the response variable.
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Submitted: September 21, 2011 Revised: July 10, 2012 Accepted: July 24, 2012
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Source: Journal of Abnormal Psychology. Vol. 122. (1), Feb, 2013 pp. 138-155)
Accession Number: 2012-32960-001
Digital Object Identifier: 10.1037/a0030060
Record: 23- Title:
- Individual and situational factors that influence the efficacy of personalized feedback substance use interventions for mandated college students.
- Authors:
- Mun, Eun Young. Center of Alcohol Studies, Rutgers, The State University of New Jersey, Piscataway, NJ, US, eymun@rci.rutgers.edu
White, Helene R.. Center of Alcohol Studies, Rutgers, The State University of New Jersey, Piscataway, NJ, US
Morgan, Thomas J.. Center of Alcohol Studies, Rutgers, The State University of New Jersey, Piscataway, NJ, US - Address:
- Mun, Eun Young, Center of Alcohol Studies, Rutgers, The State University of New Jersey, 607 Allison Road, Piscataway, NJ, US, 08854, eymun@rci.rutgers.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 77(1), Feb, 2009. pp. 88-102.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 15
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- alcohol, college students, brief intervention, personalized feedback intervention, evidence-based treatment
- Abstract:
- Little is known about individual and situational factors that moderate the efficacy of personalized feedback interventions (PFIs). Mandated college students (N = 348) were randomly assigned either to a PFI delivered in the context of a brief motivational interview (BMI; n = 180) or to a written PFI only (WF) condition and were followed up at 4 months and 15 months postintervention. The authors empirically identified heterogeneous subgroups utilizing mixture modeling analysis based on heavy episodic drinking and alcohol-related problems. The 4 identified groups were dichotomized into an improved group (53.4%) and a nonimproved group (46.6%). Logistic regression results indicated that the BMI was no more efficacious than the WF across all mandated students. However, mandated students who experienced a serious incident requiring medical or police attention and those with higher levels of alcohol-related problems at baseline benefited more from the BMI than from the WF. It may be an efficacious and cost-effective approach to provide a written PFI for low-risk mandated students and an enhanced PFI with a BMI for those students who experience a serious incident or have higher baseline alcohol-related problems. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Alcohol Drinking Attitudes; *Alcohol Drinking Patterns; *Evidence Based Practice; *Feedback; *Intervention; College Students
- Medical Subject Headings (MeSH):
- Alcohol-Related Disorders; Feedback; Humans; Life Change Events; Mandatory Programs; Psychotherapy, Brief; Social Environment; Students; Substance-Related Disorders; Treatment Outcome; Universities
- PsycINFO Classification:
- Health & Mental Health Treatment & Prevention (3300)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Readiness to Change Questionnaire DOI: 10.1037/t00434-000
Beck Depression Inventory DOI: 10.1037/t00741-000
Comprehensive Effects of Alcohol Questionnaire DOI: 10.1037/t00697-000
Rutgers Alcohol Problem Index DOI: 10.1037/t00517-000 - Grant Sponsorship:
- Sponsor: National Institute on Drug Abuse
Grant Number: DA 17552; DA 17552-05S1
Recipients: No recipient indicated
Sponsor: Rutgers Transdisciplinary Prevention Research Center
Recipients: Pandina, Robert J. (Prin Inv) - Conference:
- Annual Scientific Meeting of the Research Society on Alcoholism in Washington, 31st, Jul, 2008, Washington, DC, US
- Conference Notes:
- An earlier version of this article was presented at the aforementioned conference.
- Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Nov 17, 2008; Revised: Nov 11, 2008; First Submitted: Jan 15, 2008
- Release Date:
- 20090126
- Correction Date:
- 20130114
- Copyright:
- American Psychological Association. 2009
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0014679
- PMID:
- 19170456
- Accession Number:
- 2009-00563-017
- Number of Citations in Source:
- 88
- Persistent link to this record (Permalink):
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- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2009-00563-017&site=ehost-live">Individual and situational factors that influence the efficacy of personalized feedback substance use interventions for mandated college students.</A>
- Database:
- PsycINFO
Individual and Situational Factors That Influence the Efficacy of Personalized Feedback Substance Use Interventions for Mandated College Students
By: Eun Young Mun
Center of Alcohol Studies, Rutgers, The State University of New Jersey;
Helene R. White
Center of Alcohol Studies, Rutgers, The State University of New Jersey
Thomas J. Morgan
Center of Alcohol Studies, Rutgers, The State University of New Jersey
Acknowledgement: An earlier version of this article was presented at the 31st Annual Scientific Meeting of the Research Society on Alcoholism in Washington, DC, July 2008. This study was supported by National Institute on Drug Abuse Grants DA 17552 and DA 17552-05S1 as part of the Rutgers Transdisciplinary Prevention Research Center (Robert J. Pandina, principal investigator). We thank Katarzyna Celinska, Sarah Fink, Corey Grassl, Barbara Kachur, Brian Kaye, Lisa Laitman, Polly McLaughlin, Lisa Pugh, Kelly Pugh, and Malina Spirito for their help with the data collection and their commitment to the research project and Adam Thacker for his help with database management.
Over 40% of college students report having engaged in heavy episodic drinking (HED) at least once in the past 2 weeks, and over 20% of college students report having engaged in HED three or more times in the past 2 weeks (Wechsler et al., 2002). Consequences of excessive drinking among college students include injuries, motor vehicle accidents, unprotected sex, sexual victimization, academic problems, health problems, suicide attempts, destructive behavior, and police involvement (Engs, Diebold, & Hanson, 1994; Hingson, Heeren, Zakocs, Kopstein, & Wechsler, 2002; Presley, Meilman, & Cashin, 1996; Wechsler, Lee, Nelson, & Lee, 2001). In 2001, more than 1,700 U.S. college student deaths and over 500,000 unintentional injuries were alcohol related (Hingson, Heeren, Winter, & Wechsler, 2005). In response, a number of preventive interventions have been implemented to help college students move safely through this risky transitional developmental period between adolescence and young adulthood (i.e., emerging adulthood; Arnett, 2000, 2007; Dimeff, Baer, Kivlahan, & Marlatt, 1999). The massive growth in college prevention programs seen over the last decade (Anderson & Milgram, 1996, 2001; Wechsler et al., 2002) reflects efforts to provide universal and selective preventive interventions to college students.
Personalized Feedback InterventionsThe available evidence suggests that individually oriented, multicomponent interventions that enhance cognitive–behavioral skills, enhance motivation to change, provide accurate peer norms for alcohol use and drug use on campus, and challenge any inaccurate alcohol expectancies are efficacious for college students (Larimer & Cronce, 2002; National Institute on Alcohol Abuse and Alcoholism, 2002). In particular, personalized feedback interventions (PFIs), which are often delivered within the context of a brief motivational interview (BMI), have been shown to be efficacious with heavy drinking volunteer students (e.g., Baer, Kivlahan, Blume, McKnight, & Marlatt, 2001; Baer et al., 1992; Borsari & Carey, 2000; Carey, Carey, Misto, & Henson, 2006; Larimer et al., 2001; Marlatt et al., 1998; Murphy et al., 2001) and with mandated students (Borsari & Carey, 2005; White, Mun, Pugh, & Morgan, 2007). The theoretical rationale behind PFIs is that personalized feedback will increase the readiness of a student to change his or her drinking behaviors (Miller & Rollnick, 2002). Also, students will alter their perceptions about risk and peer use norms, as well as their alcohol/drug expectancies (Dimeff et al., 1999). These changes will lead to reduced drinking, and this reduction should reduce negative consequences of alcohol use. Therefore, when PFIs are presented within the context of a BMI, in which the counselor provides feedback in an empathetic, nonthreatening, and nonjudgmental manner, it is expected that they will increase students' readiness to change and help guide students through the change process.
Recent reviews of individual-focused interventions have found that in-person interventions that include motivational interviewing and personalized normative feedback are more efficacious than other types, such as education-focused programs (see Carey, Scott-Sheldon, Carey, & DeMartini, 2007; Larimer & Cronce, 2007). White et al. (2007) found that, over a long-term follow-up, a PFI delivered with a BMI proved to be more efficacious in reducing risky drinking and related problems for mandated college students than did a written PFI without a BMI. Few studies, however, have empirically examined moderating factors of PFIs on drinking outcomes. Thus, it is not well understood under which conditions or for whom PFIs work best (for reviews, see Carey, Scott-Sheldon, et al., 2007;Larimer & Cronce, 2007; Neighbors, Larimer, Lostutter, & Woods, 2006; Walters & Neighbors, 2005; White, 2006). It is critical that we begin to determine for whom PFIs work best and for whom we need different types of interventions. The present study is an attempt to fill this gap. It assessed whether there are individual and situational factors that moderate the efficacy of brief PFIs for mandated students over a long term.
Individual and Situational Factors That Influence PFI Efficacy Preintervention Drinking Levels
It has been suggested that PFIs may have a greater effect for heavier drinkers than for lighter drinkers because feedback for the former group is more extreme (Walters & Neighbors, 2005). However, studies have been inconsistent in their findings among nonmandated students (Larimer et al., 2007; Murphy et al., 2001). Murphy et al. compared the efficacy of a PFI within the context of a BMI and an educational intervention with that of an assessment-only control on weekly alcohol consumption and binge drinking among 84 volunteer high-risk students. They found that the PFI contributed to greater reductions in alcohol use and heavy drinking at the 3-month and 9-month follow-ups among those students who were heavier drinkers at baseline. This finding should be interpreted with caution, because Murphy et al. did not formally test the interaction between baseline drinking and PFI conditions; also, due to a small sample size, they used α = .15 as the Type I error rate. In contrast, in a large sample of volunteer students, Larimer et al. did not find that severity of baseline drinking moderated the efficacy of a mailed PFI at the 1-year follow-up. It is interesting that Larimer et al. reported that abstainers benefited more from the feedback than did drinkers. A recent meta-analysis of 62 studies (Carey, Scott-Sheldon, et al., 2007) reported that individual-level interventions were less successful when heavy drinkers or members of other at-risk groups (e.g., Greeks, athletes, first-year students) were targeted.
This apparent inconsistency regarding whether preintervention alcohol use levels play a role in PFI efficacy may be attributed to several methodological issues. First, some of the previous studies may have lacked the power necessary to detect moderation effects due to their insufficient overall sample size. Whereas many clinical trial studies are designed to have enough power to detect treatment effects (i.e., main effects), few have enough power to detect differential efficacy across subgroups (i.e., moderation effects; see Pocock, Assmann, Enos, & Kasten, 2002, for a review). The power to detect moderation is also affected by subgroup sample sizes, restriction in predictor variable range, magnitude of the moderating effect (Aguinis & Stone-Romero, 1997), and measurement error (Sackett, Harris, & Orr, 1986). Therefore, some existing studies may have inadvertently restricted the range of observations by screening out those individuals whose baseline drinking levels were at lower ends of the spectrum; this restriction may have resulted in lowered power.
Second, previously reported findings are often based on univariate/bivariate analysis, although preintervention drinking levels are generally known to be confounded with other individual and situational factors (e.g., gender). Treatment groups are typically balanced through random assignment on measured and unmeasured variables. However, any covariates that are strongly related to outcomes should be adjusted when examining treatment effects (Pocock et al., 2002). This recommendation is also applicable when examining moderation effects. Thus, adjusting for individual and situational factors related to treatment outcomes may help clarify whether preintervention drinking levels affect the efficacy of a PFI above and beyond the influences of these confounding factors.
Third, some of the existing studies categorized students on the basis of an a priori definition (e.g., those with five or more drinks in a row in the past 2 weeks, or those in the upper half of a sample on the basis of number of drinks). However, this heuristic dichotomization approach may be arbitrary. In recent studies of natural trajectories of alcohol use among adolescents and college students, heterogeneous subgroups have been empirically identified on the basis of their trajectories over time (e.g., Sher, Gotham, & Watson, 2004). The same methodology may be adopted for evaluation studies when documenting subgroups with distinctive profiles of change over time postintervention and when examining predictors and moderators of change.
Incident Seriousness
Existing studies on mandated students have not addressed the possibility that mandated students may initiate the self-regulatory self-recovery process due to their having been caught and sanctioned and that PFIs may facilitate rather than cause this self-recovery process. A few recent studies of mandated students suggest that the alcohol-related violation itself prior to any intervention contributes to reductions in alcohol use (Morgan, White, & Mun, 2008) and that perceived aversiveness of the incident is positively related to the motivation of students to change their drinking (Barnett, Goldstein, Murphy, Colby, & Monti, 2006). Barnett et al. hypothesized that salient alcohol-related events (e.g., hospitalization or medical problems) would bring about self-evaluation and greater motivation to change, especially among those with less prior experience with alcohol and fewer prior alcohol problems. Barnett et al. found, as expected, that prior alcohol use was negatively linked to incident aversiveness and that prior alcohol-related problems (AP) were negatively associated with personal attribution of the incident. In addition, greater perceived incident aversiveness was linked with greater motivation to change alcohol use. Morgan et al. (2008) provided some empirical evidence that mandated students, who had been involved in an incident that required medical or police attention, actually reduced their drinking prior to the intervention more than did those students who had been involved in a nonserious incident. Therefore, to better understand changes among mandated students, it is critical that we look at the nature of the incident when we examine the efficacy and moderated efficacy of PFIs.
Readiness to Change
The findings that an incident itself (or self-regulation following it) has an effect on behavior change (Morgan et al., 2008) underscore the need to examine students' readiness to change or motivation to change following the incident as a potential explanation for differential intervention efficacy across different individuals. The existing literature is inconclusive regarding whether readiness to change moderates the efficacy of PFIs. Although Carey, Henson, Carey, and Maisto (2007) did not find a significant moderation effect between BMI and readiness to change among volunteer students, there is limited evidence that such an effect exists. For example, Fromme and Corbin (2004) found that, at baseline, mandated participants reported higher levels of readiness to change than did volunteer students. When they tested readiness to change as a potential moderator of intervention efficacy, results showed a trend toward greater reductions in heavy alcohol consumption following the intervention, compared with the control condition, among the volunteer but not the mandated students with greater readiness to change at baseline.
Positive Alcohol Expectancies
Alcohol expectancies are defined as “structures in long-term memory that have impact on cognitive processes governing current and future consumption” (Jones, Corbin, & Fromme, 2001, p. 59). It is hypothesized that a PFI can alter one's positive alcohol expectancies and can thus reduce motivations to use and advance one's movement across the stages of change (Dimeff et al., 1999). Limited evidence exists that those who drink to enhance their social functions (positive alcohol expectancies) may benefit more from a PFI, at least in a college volunteer sample, because those individuals may be more sensitive to peer norms (Neighbors, Larimer, & Lewis, 2004). However, little is known as to whether positive alcohol expectancies are related to differential efficacy of PFIs among mandated students.
Gender
A number of studies have looked at gender as a potential moderator of PFI efficacy among volunteer college samples, and the results have been equivocal. Murphy et al. (2004) found that women in both PFI conditions with and without a motivational interview lowered their weekly drinking at the 6-month follow-up, whereas men did not reduce their drinking in either condition. Similarly, Chiauzzi, Green, Lord, Thum, and Goldstein (2005) reported that, although volunteer students who received a PFI were not statistically different from students in the control group overall, a subset of heavy drinking women in the PFI condition reduced their total drinks and HED during special occasions more than did their heavy drinking counterparts in the educational control condition. In contrast, there were no such group differences among men. However, several other studies have found no gender differences in response to PFIs (e.g., Carey, Henson, et al., 2007; Marlatt et al., 1998). For example, Marlatt et al. reported that, although women overall reported significantly more declines in AP than did men, men and women responded similarly to a PFI. Thus, it is generally unclear whether a relative advantage for women exists following a PFI.
First-Year Student in College and Other Drug Use
First-year students in college are generally considered to be at risk for excessive alcohol use. Although evidence of the efficacy of PFIs exists for first-year college students (see Larimer & Cronce, 2007), PFIs may be less beneficial for first-year students, according to Carey, Scott-Sheldon, et al. (2007). However, it is unclear whether the efficacy of PFIs works differently for first-year students, compared with non-first-year students, when their different patterns of alcohol use are controlled. In addition, many mandated students are caught for drug use. The current study investigated whether other drug use at baseline moderates PFI efficacy among mandated students.
The Current StudyThe current study sought to examine (a) whether some mandated students reduce alcohol use more following a PFI than do other students and (b) whether some students respond better to a PFI delivered in the context of a BMI than to a written PFI only. In this study, we aimed to extend an earlier study with the same sample (White et al., 2007) by empirically identifying heterogeneous subgroups of mandated students that differentially respond to a PFI. To achieve this goal, we analyzed HED and AP on the basis of their change patterns, as well as their overall levels. We did so using the latent change score approach proposed in a recent study (Mun, von Eye, & White, in press) and an extension of mixture modeling analysis. HED and AP were chosen because reductions in these alcohol use behaviors reflect self-regulated harm reduction better than do other alcohol use measures. We used empirically identified groups as the outcome variable in subsequent logistic regression analyses. We formally tested the following six individual and situational factors as predictors of change in the context of a PFI: incident seriousness, readiness to change, positive alcohol expectancies, gender, first-year student, and other drug use. We then examined whether the efficacy of a PFI delivered in the context of a BMI and the efficacy of a written PFI differed depending on individual and situational factors, as well as baseline HED and AP (i.e., differential efficacy of the PFI types by individual and situational factors). Thus, we tested for moderation effects by examining the interaction between PFI condition and each of the predictors.
MethodParticipants
Participants were students mandated to a university Alcohol and Other Drug Assistance Program due to their infractions of university rules about alcohol and drug use in residence halls. The sample was recruited during the fall semester 2003 and spring and fall semesters 2004. Of the 390 mandated students, 24 (6.2%) were ineligible for the study on the basis of the following exclusion criteria: prior substance abuse treatment, a score greater than 13 on the Beck Depression Inventory (Beck & Steer, 1984), a .24% blood alcohol concentration (BAC) or higher in a typical week, more than 10 occasions of HED (five or more drinks on one occasion for men and four or more for women) in the past month, nine or more alcohol/drug-related negative consequences, near-daily marijuana use, or abstinence from alcohol and drugs (i.e., they were caught in a room with alcohol or drugs but had never used them themselves).
Because this was a randomized study and there was no prior research to support the efficacy of written feedback alone for mandated students, the highest risk students were excluded for ethical and clinical reasons. All of these high-risk students received an in-person intervention. In addition, only first offenders were eligible for the study. Another 18 students (4.9%) declined to participate in the research study, which left a final sample of 348 students (see Figure 1 for participant flow). The resulting sample was 60.1% male, and most students were in their first (61.6%) or second (29.9%) year of college. The sample was 79% Caucasian, 15.5% Asian American, 2.2% African American, and 3.4% other or mixed ethnicity. Over 90% of participants were caught violating residence life rules while in a group, and 88.6% were referred for alcohol-related violations (for greater detail on sample characteristics, see White et al., 2006, 2007).
Figure 1. A flowchart of recruitment, participation, and follow-up rates.
Procedures and Interventions
All students referred to the Alcohol and Other Drug Assistance Program completed a baseline assessment questionnaire. Using data from the initial assessment, we determined eligibility and created an individualized profile for each eligible student. The personal profile included information on peer norms for alcohol and drug use, typical peak BAC, alcohol- and drug-related problems, alcohol expectancies, high-risk behaviors (e.g., driving under the influence, unplanned sex after using alcohol or drugs), and personal risk factors (e.g., depression, family history of alcoholism). In addition, the profile contained general information about the effects of various BAC levels and tolerance to alcohol. Students returned approximately 1 week later and were randomly assigned (by a flip of a coin) either to a BMI condition (n = 180, 51.7%) or to a written feedback only (WF) condition (n = 168, 48.3%).
Students in the BMI condition met individually with a counselor and discussed their written personal profile, which they were given to take home. The counselor provided feedback in an empathic, nonconfrontational, and nonjudgmental style based on the principles of motivational interviewing (Miller & Rollnick, 2002). Students in the WF condition were handed their written profile, and they left without discussing it with their counselor. Intervention fidelity was assured in several ways. First, counselors were trained specifically in motivational interviewing techniques and received weekly supervision from Thomas J. Morgan, a clinical psychologist with expertise in motivational interviewing techniques. Second, five BMI and two WF sessions for each counselor were audiotaped. The supervising clinical psychologist listened to the audiotapes and provided feedback to the counselor. Third, the counselors completed a therapist checklist after each BMI session. The checklist consisted of the therapeutic tasks during the session, as well as a self-evaluation for the counselor that focused on being empathic and nonjudgmental and providing support to the student. The clinical supervisor reviewed the checklists to ensure that the counselors adhered to the protocol.
Students were followed up approximately 4 months after the second session (n = 319, 91.7%) and again 15 months postbaseline (n = 220, 63.2%). There were no significant differences between those who were followed up and those who dropped out on demographic or baseline alcohol use characteristics (see White et al., 2007, for means and standard deviations).
Measures
Alcohol use variables
Students reported the number of HED occasions they had experienced in the past month (defined as five or more standard drinks for men and four or more for women; Wechsler et al., 2002). The number of AP was obtained from the 18-item short version of the Rutgers Alcohol Problem Index (RAPI; White & Labouvie, 1989, 2000). The RAPI has demonstrated reliability and discriminant construct validity in both general population and clinical samples of adolescents and young adults (White, Filstead, Labouvie, Conlin, & Pandina, 1988; White & Labouvie, 1989, 2000), and the 18-item version correlates above .9 with the 23-item version (White & Labouvie, 2000). Students reported on the total number of AP experienced in the last 3 months (αs = .73–.80 across the three assessments). The distributions of the HED and AP were positively skewed and leptokurtic. They were subsequently log-transformed after a constant of 1 was added to normalize skewed distributions.
The self-report alcohol use measures we used in the current study are widely used in the literature on college drinking and have been found to be reliable when corroborated by collateral reports (Borsari & Carey, 2005; Marlatt et al., 1998). Other studies of college student drinking and related problems have shown that use of collateral reports does not improve validity of the data (Carey et al., 2006; Marlatt et al., 1998).
Incident seriousness and demographic variables
The incident for which the student was mandated was coded as “serious” (coded 1) if the referral was made by emergency medical services/hospital (15.3%) or law enforcement personnel (2.3%) and as “nonserious” (coded 0) if the student was referred by a residence hall advisor (83.4%). Gender was coded 1 for men and 0 for women. First-year students were coded 1, and all others were coded 0. Other drug use (cigarettes, marijuana, and other substances) at baseline was coded 1 for those with any use of any substance and 0 for those without any use in the past month. Existing studies have found that students provide valid self-report drug use data (e.g., Johnston, O'Malley, Bachman, & Schulenberg, 2007).
Readiness to change
Readiness to change was measured at baseline by the Readiness to Change Questionnaire (RCQ; Heather, Rollnick, & Bell, 1993). The RCQ is a 12-item self-report measure designed to provide a single stage of change assignment (precontemplation, contemplation, or action) as well as a continuous score for each of the three stages of change. Items (e.g., “I am trying to drink less than I used to,” “I enjoy my drinking, but sometimes I drink too much”) were presented on a 5-point Likert scale that ranged from strongly disagree to strongly agree. In the present study, four items capturing the precontemplation stage were reverse coded and were averaged with the other items to create a continuous scale score (α = .88 at baseline). Higher scores reflect the greater readiness of a person to start to change or to actually be changing his or her drinking habits.
Positive alcohol expectancies
Alcohol expectancies were measured at baseline by the Comprehensive Effects of Alcohol Questionnaire (CEOA; Fromme, Stroot, & Kaplan, 1993). The CEOA consists of 20 positive and 18 negative expectancy items. Positive alcohol expectancies included items related to tension reduction, sexuality, liquid courage, and sociability factors. Example items from each factor, respectively, are “I would feel calm,” “I would be a better lover,” “I would be courageous,” and “I would act sociable.” Students responded on a 4-point Likert-type scale ranging from disagree to agree. We administered only 8 positive expectancy items out of the original 20 positive items in order to lessen the burden of students of filling out a lengthy questionnaire (we used the 2 items with the highest factor loadings from each of the four factors; Fromme et al., 1993). The 8 items were averaged to create a positive alcohol expectancy score. Higher positive expectancy scores reflect stronger beliefs that consuming alcohol would result in positive effects for the participant (α = .73 at baseline).
Social desirability
We included a 13-item shortened version (Reynolds, 1982) of the original Marlowe–Crowne Social Desirability Scale (Crowne & Marlowe, 1960) that assesses the tendency of a person to present himself or herself in a socially desirable way. This short version has been found to discriminate criminal and noncriminal groups and been known to have acceptable test–retest reliability and internal consistency (Andrews & Meyer, 2003). We included this social desirability scale in the baseline assessment to control for potential demand characteristics among mandated students in reporting substance use. Example items are “I'm always willing to admit it when I make a mistake,” “I have never deliberately said something that hurt someone's feelings,” and “I have never been annoyed when people expressed ideas very different from my own.” Responses were coded 1 for “true” and 0 for “false” responses. The scale score was created by summing responses (α = .66 at baseline). High scores indicate higher levels of social desirability. Mandated students may be more motivated to underreport alcohol use levels than are volunteer students.
In a previous study, we reported from a different sample that mandated students with high demand characteristics tended to report lower levels of alcohol and drug use (White et al., 2008). Therefore, although there was no difference in social desirability between two PFI conditions at baseline with the present sample (White et al., 2007), we controlled for social desirability mean levels (and variances) by constraining them to be equal across classes in mixture analysis.
Missing Data
We used the expectation maximization algorithm for maximum likelihood estimation to impute missing data with SAS, after the Little's chi-square test of missing completely at random (Little, 1988) had a nonsignificant result, χ2(8020) = 8,078.96, p > .05. This result indicated that missing values were a random subset of the complete data. Thus, we deemed that the imputed data were unbiased (Little & Rubin, 1987; Schafer, 1997).
ResultsWe utilized a latent change score approach based on latent curve models. The previous study (White et al., 2007) showed that overall substance use decreased between baseline and the 4-month follow-up assessment and increased between the 4-month and 15-month follow-up assessments. Instead of analyzing this change pattern with typical nonlinear latent curve models, we examined latent changes between baseline and the first follow-up at 4 months (i.e., latent change variable; see Figure 2) and between 4 months and 15 months postintervention (i.e., latent change variable). We specified the outcome levels at 15 months postintervention as the intercept level (i.e., latent variable) because participants in the present study were randomly assigned to a treatment condition and there were no group differences between the BMI and WF groups at baseline. Thus, we focused on the long-term outcome levels rather than baseline levels. This intercept selection approach is equivalent to centering a time metric variable at 15 months in latent curve models (see the Appendix). All latent variable analyses were conducted with Mplus Version 5.0 (Muthén & Muthén, 1998–2007), and subsequent logistic regressions were conducted with SPSS Version 16.
Figure 2. Analyzed mixture models using latent change variables. Solid lines indicate directly estimated parameters, and dotted lines indicate either fixed parameters (i.e., factor loadings) or a mixture part of the analyzed model (i.e., class to latent variables). Social desirability was constrained to be equal in mean, variance, and paths across classes. Level = outcome levels at 15 months postintervention; IC = initial change from baseline to 4 months postintervention; SC = subsequent change from 4 months to 15 months postintervention.
Latent Change Score Analysis With Mixture Modeling Analysis and Outcome Groups
We analyzed the number both of HED and of AP over time simultaneously using mixture analysis. We added social desirability as a covariate to ensure that derived groups were equivalent in social desirability. Results indicated that, on the basis of the Bayesian information criterion (BIC), the model with four latent classes was the best fitting, most parsimonious model (BIC2 = 5,654.72, BIC3 = 5,604.54, BIC4 = 5,432.33, and BIC5 = 5,465.26 for two-, three-, four-, and five-latent class models, respectively). The four latent classes were very well separated (entropy = .99), and the average posterior probability for the most likely class exceeded .98. Entropy values approaching 1 are considered to indicate well-separated classes (Celeux & Soromenho, 1996). Figure 3 clearly illustrates that a considerable number of students (Classes 1 through 3) continued to engage in HED (see Figure 3A) and to report AP (see Figure 3B) throughout the observed period. In the present study, we decided to focus on those who improved versus those who did not, primarily to increase power and improve accuracy of parameter estimates in detecting predictors and moderators in subsequent logistic regression analyses. For instance, cross-tabulating incident seriousness with the four classes resulted in a few cells with a limited number of observations, especially for the smallest class (Class 1). Previous studies in the literature have combined empirically identified groups into a smaller number of groups on the basis of other practical and conceptual considerations (e.g., Bongers, Koot, van der Ende, & Verhulst, 2004) or have rejected alternate solutions from considerations based on additional criteria (e.g., minimum cluster size [5% or more], distinctively shaped trajectories, or large bivariate residuals). Therefore, in all subsequent analyses, Classes 1 through 3 were combined into a single nonimproved group (n = 162, 46.6%). The remaining group was labeled as an improved group (n = 186, 53.4%).
Figure 3. Estimated mean growth trajectories of heavy episodic drinking (HED; Figure 3A) and alcohol-related problems (AP; Figure 3B) for the four-class models specified as shown in Figure 2. In all subsequent analysis, Classes 1 through 3 were combined into a single nonimproved group, and Class 4 was classified as an improved group. pp = the average posterior probability for the most likely class; T1 = baseline; T2 = 4 months postintervention; T3 = 15 months postintervention.
Table 1 shows the means and standard deviations of the alcohol use outcome variables at the three time points for these two groups. The improved group reported significantly lower levels of HED and AP at baseline as well as at the two follow-up assessments. Table 2 shows the within-person changes (mean changes, t values, and effect sizes) from baseline to 4 months, from 4 months to 15 months, and from baseline to 15 months postintervention from paired t tests. The improved group, compared with the nonimproved group, showed reductions in the range of medium-to-large effect sizes (see Table 2; d = 0.68–0.77; d = 0.2, 0.5, and 0.8 for small, medium, and large effects; Cohen, 1988) from baseline to 4 months, followed by significant upward swings in the range of small-to-moderate effect sizes (d = 0.41–0.61). The initial reduction in AP was maintained over time for the improved group; however, the initial reduction in HED was not maintained over the long term. When followed up at 15 months postintervention, the improved group reported lower levels of AP, but not of HED, compared with its baseline levels. The nonimproved group did not improve in HED or AP over the long term. It had higher levels of HED and AP than did the improved group at all times, and the only positive outcome for this group was the initial reduction in AP from baseline to the 4-month follow-up.
Means and Standard Deviations of Heavy Episodic Drinking (HED) and Alcohol-Related Problems (AP) for the Improved and Nonimproved Groups
Within-Individual Change in Heavy Episodic Drinking (HED) and Alcohol-Related Problems (AP) Following Brief Interventions (N = 348)
Predictors of Change in the Context of the PFI
First, we used univariate logistic regression without controlling for any covariates to investigate whether each of the individual and situational factors, as well as intervention condition and baseline HED and AP, significantly predicted improved group membership. As expected, all individual and situational factors, with the exception of readiness to change, significantly predicted improved group membership when we examined these factors separately in univariate logistic regression analysis without adjusting for any other individual and situational variables or baseline HED and AP (see the first column in Table 3). Also, as expected, baseline HED and AP levels were significantly associated with improved group membership.
Individual and Situational Factors as Predictors of Change in the Context of the PFI
Next, we added baseline HED and AP levels to each model to statistically control for their effects, in order to examine whether the individual and situational factors uniquely contributed to improved group membership above and beyond the influences of baseline HED and AP. Thus, including these covariates enabled us to infer predictors of change that were not confounded with preintervention drinking levels. In other words, we examined, given the same levels of HED and AP at baseline, whether the individual and situational factors predicted improved group membership. When we adjusted for baseline HED and AP (see the middle column in Table 3), we found that experiencing a serious incident, reporting greater readiness to change, being female or a non-first-year student, and reporting no other drug use significantly predicted improved group membership. Baseline AP and positive alcohol expectancies no longer significantly predicted improved group membership. Positive alcohol expectancies and AP at baseline may largely be accounted for by baseline HED. It is interesting that greater readiness to change was a significant predictor of improved group membership only after baseline HED and AP levels were taken into consideration. This result suggests that, given the same levels of HED and AP, those with greater readiness to change were more likely to be in the improved group.
When all variables were examined simultaneously in a single multivariate model (see the last column in Table 3), results indicated that reporting lower levels of HED at baseline, experiencing a serious incident, and being female were the only significant predictors of improved group membership. Other individual and situational factors (e.g., reporting greater readiness to change and positive alcohol expectancies, being a first-year student, and reporting other drug use) did not uniquely predict improved group membership when statistical adjustment was made to remove confounding influences. These findings indicate that because many individual and situational factors are somewhat related, their unique contributions to intervention outcomes cannot be comprehended fully through use of univariate analysis alone. For example, greater readiness to change no longer significantly predicted the outcome, in part because it was related to incident seriousness (r = .32, p < .01) and its effects were confounded with incident seriousness. In contrast, the advantage for female students and for those who experienced a serious incident persisted above and beyond their other co-occurring individual and situational factors and different preintervention drinking levels. Note that, in all three analyses, we found that having received the BMI did not predict improved group membership.
Moderators of the PFI Efficacy Across the BMI and WF Conditions
We then examined whether PFI efficacy was different across the BMI and WF conditions depending on individual and situational factors. In other words, we tested for moderation effects (i.e., differential efficacy of the PFI types by individual and situational factors). In addition to the six individual and situational factors, we examined baseline levels of HED and AP as potential moderators. All continuous variables were centered in order to avoid potential multicollinearity problems, and interaction terms were created with centered variables. We added each interaction term one at a time to the final multivariate model shown in Table 3.
Incident seriousness and AP at baseline were linked to the differential PFI efficacy across the BMI and WF conditions. Of the mandated students who experienced a serious incident, those who were assigned to the BMI were more likely to be in the improved than the nonimproved group, log odds ratio (logit) = 1.56, odds ratio (OR) = 4.76, 95% confidence interval (CI) = 1.21–18.66, p < .05 (see Figure 4 for simple slopes). In addition, of the mandated students who reported higher levels of AP at baseline, those who were assigned to the BMI were more likely than were those in the WF to be in the improved group, logit = 1.43, OR = 4.18, 95% CI = 2.02–8.64, p < .01 (see Figure 5 for simple slopes). All other individual and situational factors were not statistically significant moderators.
Figure 4. The interaction of PFI condition with incident seriousness. The BMI is more efficacious than the WF for mandated students who were referred after a serious incident. PFI = personalized feedback intervention; BMI = brief motivational interview; WF = written feedback only.
Figure 5. The interaction of PFI condition with baseline level of alcohol-related problems (AP). The BMI is more efficacious than the WF for mandated students with higher levels of AP at baseline. BMI = brief motivational interview; WF = written feedback only.
DiscussionThis study examined whether subgroups exist in the response of mandated students to a PFI and whether different PFIs are differentially efficacious for mandated college students. We found heterogeneous subgroups with distinctively different outcome trajectories. Overall, we found that the majority of the mandated students (53.4%) improved in both HED and AP after the PFI regardless of whether they were assigned to the BMI or the WF condition. The nonimproved group consisted of 46.6% of the mandated students who improved neither in HED nor in AP over the long term. This group may represent individuals who have chronic drinking problems and resist changes in their drinking. However, it is noteworthy that the mandated students in the current study were relatively low-risk individuals compared with participants in other studies that screened and selected high-risk volunteer students (e.g., Chiauzzi et al., 2005; Murphy et al., 2001; Walters, Roudsari, Vader, & Harris, 2007) or other studies of mandated samples (e.g., Barnett et al., 2006) because of our study's clinical exclusion criteria. For example, many of the students in Barnett et al. had been mandated for more serious infractions than ours (e.g., 82% were referred for acute intoxication or an alcohol-related injury; only 15% of our students were referred for an incident that required police or medical attention).
Predictors of Change, Moderators of the PFI Efficacy, and Clinical Implications
The findings from the present study may provide some answers to the inconsistent findings in the literature. Our findings indicated that it was lighter drinking individuals who improved more following the PFI over a long-term follow-up. This finding is consistent with a recent conclusion from a large meta-analysis that PFIs are more beneficial for lighter drinking individuals (Carey, Scott-Sheldon, et al., 2007). We also found that there was no overall difference in the efficacy between the BMI and WF groups. Therefore, for mandated students whose baseline levels of HED or AP are low, written or Web-based personalized feedback may be a cost-effective way to deliver a PFI as a selective intervention. The present study also demonstrated that the advantage for those who are female, who experienced a serious incident, or who engaged in less frequent HED at baseline was maintained even after we took into account other individual and situational factors as well as preintervention AP levels. In previous studies, it has been difficult to assess to what extent ensuing reductions are due to unique effects of individual and situational factors, above and beyond other confounding variables. In the present study, findings for the effects of being a female, experiencing a serious incident, or being a less heavy drinker cannot be considered an artifact of omitted baseline confounding factors because these confounded effects were statistically adjusted. There may be other factors that were not considered in the present study. However, the individual and situational factors considered in the present study, as well as the alcohol use controls, represent most of the factors that have been discussed in the literature.
The present study also suggests that it may still be a valuable goal for interventionists to improve readiness to change, as well as to reduce other drug use. In particular, greater readiness to change, being a non-first-year student, and no other drug use at baseline predicted a better intervention outcome when baseline HED and AP levels were statistically controlled but not when other individual and situational factors were examined simultaneously. Although this finding indicates that there were no unique effects of these variables above and beyond other co-existing individual and situational factors, targeting these co-existing factors might improve intervention outcomes. Whereas experiencing a serious incident and, certainly, being a first-year student cannot be subject to change by interventions, early preventive interventions with incoming students might help them reduce problematic behaviors that may lead to serious incidents.
In our analysis of the moderated PFI efficacy, we found that experiencing a serious incident prior to the PFI and reporting high levels of AP at baseline were statistically significant moderators of the PFI efficacy favoring the BMI over the WF. These findings indicate that there is an additional benefit of an in-person, face-to-face motivational interview for students who have experienced a serious incident or have reported higher levels of AP at baseline. Other individual and situational factors—gender, first year in college, other drug use, readiness to change, and positive alcohol expectancies—did not moderate the efficacy of the BMI. In addition, the BMI efficacy did not differ across different levels of HED (i.e., no interaction effect was found between BMI and baseline HED).
In a previous study, we reported that there were no group differences in intermediate-term (4 months postintervention) alcohol reductions across the BMI and WF conditions and discussed that, given the cost of administering an in-person motivational interview in terms of time and staffing, provision of a written feedback alone may be a cost-effective way of reducing alcohol use among mandated college students (White et al., 2006). However, the more long-term follow-up study from the same sample demonstrated that additive benefits of providing an in-person BMI exist for AP above and beyond the benefit from a normative written feedback alone for the mandated students (White et al., 2007). On the basis of findings from the present study, we conclude that not all mandated students benefit additionally from an in-person BMI. Mandated students with lower levels of AP and HED may benefit just as much from a written personalized feedback alone as from an in-person BMI.
The current finding that the BMI was no more efficacious than the WF appears to differ from the previous finding (White et al., 2007). The difference in the findings may be understood in the context of differences in our approaches. First, in the present study, the PFI efficacy was assessed in terms of whether mandated students could be considered as an improved case. In contrast, in the previous study we measured the PFI efficacy using quantitative increments in each outcome variable unit. Therefore, statistically significant treatment group differences from the previous study may not sufficiently translate into a case of improvement (i.e., qualitative distinction), as defined in the current study. Second, in the present study we analyzed HED and AP simultaneously when we identified heterogeneous subgroups. In the previous study, we had examined each behavioral outcome separately. Perhaps the best way to understand the findings from these two studies is that, incrementally, the BMI was more efficacious than the WF, especially for AP. However, there was no clear advantage of the BMI over the WF across all individuals when we defined the efficacy outcome as a qualitatively distinct, categorical improvement. It is important to highlight that the BMI was more efficacious than the WF selectively for certain mandated students in the current study. That is, for those mandated students who had experienced a serious incident or whose levels of AP at baseline were high, an in-person brief motivational interview was more efficacious than was written feedback alone. These findings underscore the importance of better understanding the goodness of fit between necessary components of evidence-based treatments and different groups of students with different needs.
Limitations and Contributions
The current findings must be interpreted with caution in light of several limitations. First, we studied mandated students and did not have a true no-treatment control group (because of ethical considerations and program requirements). This restriction most likely decreased our power to detect stronger intervention effects, although our effect sizes were comparable to those of other studies on mandated and volunteer students (see Carey, Scott-Sheldon, et al., 2007; Larimer & Cronce, 2007; see also Barnett & Read, 2005, for likely reasons). In addition, the absence of a true control group prohibited us from attributing change to a PFI. Thus, we interpreted individual and situational factors as the predictors of change in the context of a PFI.
Second, findings from studies of mandated students, including the current study, may need to be understood in the context of being mandated. Two recent studies from a different sample that compared a WF with a delayed treatment control reported that mandated students reduced alcohol use on their own prior to the PFI (Morgan et al., 2008), and there were no differences between students who received the WF and those who did not at 2 months postbaseline (White et al., 2008). Therefore, reductions in alcohol use among mandated students postintervention may be attributed in part to cognitive and affective reactions to the incident for which they were mandated and subsequent self-regulation. In the present study, we did not have sufficient data on students' alcohol use following the incident but prior to the PFI. Thus, it is unclear to what extent that students had self-regulated their drinking behaviors on their own prior to the PFI. Nonetheless, the findings from the present study suggest that, following a serious incident, mandated students tended to reduce their HED and AP over the long term, especially if they received the BMI. In addition, mandated students with high levels of AP before the incident were more likely to be among the improved students if they were assigned to the BMI.
Third, on a related issue, we did not measure how intoxicated students were when caught or how aversely or seriously students perceived the incident for which they were mandated. More detailed information regarding the nature and subjective evaluation of the incident would facilitate better understanding of the efficacy of PFIs among mandated students. Fourth, the sample consisted of primarily white and Asian American students, and the findings may not generalize to other ethnic/racial groups. In addition, the findings may not be generalized to other mandated student populations with different university policies on alcohol and other drugs and policy enforcement practices (see Barnett et al., 2008).
Fifth, we found from mixture analysis that heterogeneity existed even among the nonimproved group. Factors that differentiate the three subgroups and their long-term trajectories may be of interest for the development of more intensive treatment models for these high-risk groups. The present study did not have sufficient sample size for us to conduct comparative analysis on these groups. A larger scale study of mandated students will allow researchers to examine whether the three distinctive groups, identified on the basis of statistical considerations such as the BIC and entropy statistics, can be useful in practice (Everitt, Landau, & Leese, 2001; Muthén & Muthén, 2000).
Finally, we examined the potential moderators one by one in the current study because little is known about predictors and moderators of the PFI efficacy in the literature. In addition, power to detect moderation effects is well known to be low (McClelland & Judd, 1993; Sackett et al., 1986). Given that identifying different subgroups is critical for screening, triaging, and implementing cost-effective interventions for those students in need, we adopted an exploratory approach in the current study. In a larger scale study designed to test moderation effects, it would be preferable to examine potential moderators simultaneously to understand their unique contributions.
Despite these limitations, the present study contributes to prevention research for alcohol use and AP among emerging adults and, more broadly, to evidence-based treatment research. First, the present study sheds new light on predictors of change in the context of a PFI and the efficacy of an in-person PFI delivered within a BMI among mandated students. On the basis of these findings, we suggest that it may be more cost-effective to deliver a written or Web-based PFI for low-risk mandated students and to provide an enhanced PFI with an in-person BMI for those students who have experienced a serious incident or have higher levels of AP at baseline. A two-session intervention utilizing an in-person motivational interview with personalized normative feedback presents a relatively low-cost psychological intervention. However, findings from the current study suggest that, even at low cost, an in-person BMI does not provide an additional benefit over a written PFI for many low-risk mandated students. Therefore, more research that could further identify other important moderators of PFIs among mandated and volunteer students is sorely needed to identify which students require which types of interventions.
Second, this study draws attention to the utility of a person-oriented approach (Bergman & Magnusson, 1997; von Eye & Bergman, 2003) for evaluation research and of the integrative strategy between person-oriented and variable-oriented approaches (Bates, 2000) to clinical research more broadly. As Foster, Dodge, and Jones (2003) discussed, many prevention and treatment studies are conducted from a variable-oriented perspective. Foster et al. illustrated that although studies that utilize a variable-oriented approach allow one to measure cost-effectiveness per one unit improvement in a single outcome measure, it is difficult to answer whether the cost of interventions outweighs benefits when the emphasis lies not on persons but on variables. It is especially challenging when outcomes co-occur. Foster et al. therefore suggested that a person-oriented outcome may be used as a global measure of cost-effectiveness for prevention research.
Use of two related outcome measures (e.g., AP and HED) to identify heterogeneous subgroups may be more insightful than use of an isolated single outcome when one is assessing either clinical significance at the individual level or global cost-effectiveness. Recent advances in longitudinal research methodology (see Foster & Kalil, 2008) provide attractive analytic options for evidence-based intervention research. In future, the refined focus on subgroup analysis utilized in the present study may be beneficial in the tailoring of necessary intervention components to those who need them the most.
Footnotes 1 The data reported in Morgan et al. (2008) are based on a later study (White, Mun, & Morgan, 2008) that compared a WF with a no treatment wait list control. The questions regarding students' alcohol use 30 days prior to the incident were asked very late for this study. Therefore, unfortunately, only about one third of the sample provided responses. Given the added requirement of nonoverlapped time referents, we did not have sufficient data to report on the role of the incident on alcohol use reductions. However, on the basis of evidence reported in Morgan et al. (2008), it is likely that the mandated students as a group reduced alcohol use on their own prior to the PFI, especially if they were mandated following an incident requiring medical/police attention.
2 In comparison with repeated-measures analysis of variance (ANOVA), latent curve models are a better use of the available data for evaluation studies because latent curve models tend to be more powerful and flexible, and they do not require unreasonable assumptions (see Curran & Muthén, 1999; Muthén & Curran, 1997). Simulation studies have demonstrated that latent curve models are more powerful in detecting change than are ANOVAs for a one-outcome series (e.g., Fan, 2003; Muthén & Curran, 1997), and a latent variable modeling approach has been noted as a flexible integrative analytic frame in which both fixed and random effects for linear, as well as nonlinear, outcomes are easily analyzed (Raykov, 2007; Skrondal & Rabe-Hesketh, 2004). Mun et al. (in press) demonstrated that latent curve models that use latent change scores can be specified to yield overidentified, testable models that are tailored to examine posttreatment effects or long-term follow-up effects for the analysis of data collected using pre–post–post designs. In addition, Mun et al. observed that mixture models would be a nice extension with which to examine heterogeneous subgroups that respond to a treatment in distinctively different manners in evaluation studies. The present study includes two related repeated-measures outcomes within a mixture analysis application.
3 We did not have the data on recidivism because individuals with a prior history of being mandated were not eligible to participate in the study. Note that Barnett, Murphy, Colby, and Monti (2007) reported that 15.8% of their mandated students were caught again. However, it is difficult to extrapolate the recidivism rate of this sample from other studies, due to differences in sample characteristics, university policies, and enforcement practices.
4 The skewness/kurtosis coefficients across the three assessments were 1.87/4.14, 2.87/10.39, and 2.58/8.63 for HED and 1.37/1.83, 2.93/11.08, and 2.00/4.35 for AP. After the log-transformation, the distributions were normalized. The resulting skewness/kurtosis coefficients from the transformed data across the three assessments were 0.54/−0.82, 1.06/0.20, and 0.68/−0.55 for HED and 0.19/−1.17, 1.27/0.78, and 0.70/−0.65 for AP.
5 Budd and Rollnick (1996) showed that the RCQ items can be rescored to create a continuous measure of readiness to change that has adequate reliability and predictive validity. In addition, a critical review by Carey, Purnine, Maisto, and Carey (1999) suggested that readiness to change may be more appropriately conceptualized as a continuous construct rather than as a discrete stage of change. A number of studies have utilized a continuous overall score (e.g., Carey, Henson, et al., 2007; Fromme & Corbin, 2004). In addition, the stages of change approach resulted in an inadequate number of observations for logistic regression due to a seriously unbalanced number of observations across the three stages in the current study. The majority of the students were in the precontemplation stage (67%) or action stage (29%). Only 4% of the students were in the contemplation stage. Furthermore, the correlations between the three stage scores and the continuous scale scores for readiness to change were very high and were in the expected direction. For the precontemplation, contemplation, and action stages, respectively, they were −.73, .84, and .88 (p < .05).
6 Note that the measured social desirability in this study reflects dispositional styles. The situation or context of the intervention program for mandated students may draw additional demand characteristics and may elicit socially desirable responses that are quite different from the individual dispositional tendency.
7 With three assessments, we could examine postintervention changes only linearly between the intervention and the 4-month follow-up assessment and between the 4-month and 15-month assessments postintervention. For the change process during the 15-month period following a PFI, a quadratic trajectory could be an alternative, in principle, to the latent change score approach shown in this study. However, polynomial nonlinear trajectories are unbounded with respect to time and do not reach an asymptote (see Curran & Willoughby, 2003). In addition, polynomial interpolation between assessments is necessary with a quadratic trajectory model. We concluded that with three assessments, potential extrapolation and interpolation errors could not be detected by the data and that discrete linear latent changes would be more appropriate for analysis than would continuous nonlinear trajectories. A study with more intensive assessments pre- and postintervention would be necessary to truly answer this interesting question.
8 We also analyzed HED and AP separately. The analysis of HED alone resulted in the same classification as that in the analysis that included both of the alcohol use measures. The analysis of AP resulted in similar patterns of changes but with more students who could be classified into improved cases. When the improved and nonimproved groups from each analysis were cross-tabulated into four groups and all subsequent analyses were carried out, almost all of the major findings reported in this study, including the two statistically significant moderators, were observed. Although the two approaches resulted in the same overall conclusion, we decided to report the findings from the analysis of two outcome measures conducted at the same time because doing so is statistically more parsimonious and simpler in interpretation. In addition, from the interventionist's perspective, clinical significance exists in empirically detecting subgroups on the basis of two harmful alcohol-use behaviors, rather than a single behavior isolated from the other. In all analyses, we used increased random sets of starting values of up to 100 for the initial stage and of up to 10 for the final stage optimization to avoid convergence of the final solution on local maxima. For the selected model, we increased these numbers to 1,000 and 50, respectively.
9 The Mplus program produces two separate plots for each repeated-measures outcome. The two panels in Figure 3 were from the analysis based on the simultaneous analysis shown in Figure 2.
10 Note that mixture modeling analysis is generally exploratory. It is increasingly clear that groups from mixture analysis do not necessarily provide evidence of a taxonic structure from a confirmatory analytic perspective but rather provide evidence of potentially useful, exploratory groups (Bauer, 2007; Bauer & Curran, 2003, 2004; Mun, Windle, & Schainker, 2008; Muthén, 2003; Muthén & Muthén, 2000; Sampson & Laub, 2005; von Eye & Bergman, 2003).
11 In the present study, the family-wise Type I error rate was not protected using an overly conservative procedure, such as the Bonferroni adjustment procedure, because the present study had a moderate sample size and because a trade-off exists between Type I and Type II error rates. The Bonferroni procedure is well known to be extremely conservative, and it thus has very little power for detecting true relations. Given that little is known about predictors and moderators of the PFI efficacy in the literature and that Type II error rates to detect moderation effects are high (McClelland & Judd, 1993; Sackett et al., 1986), we reasoned that the practical importance of an effect can be distinctively different from its statistical significance (or statistically defined small, medium, and large effect sizes; for a more detailed discussion, see McCartney & Rosenthal, 2000) and that it is important to balance between these two important considerations.
12 The ORs (95% CIs) for the other nonsignificant interaction terms were 1.79 (0.83–3.89), 1.27 (0.88–1.85), 0.68 (0.41–1.11), 1.00 (0.36–2.74), 1.45 (0.53–3.98), and 0.65 (0.24–1.78), respectively, for BMI × HED at Baseline, BMI × Readiness to Change, BMI × Positive Alcohol Expectancies, BMI × Female, BMI × First-Year Student, and BMI × Other Drug Use. When all main effects and interaction effects were simultaneously tested in a single model, the BMI × Incident Seriousness interaction effect was no longer significant (p > .05). The BMI × AP interaction effect remained significant (p < .05).
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APPENDIX APPENDIX A: Specification of Factor Loading Matrix
With three latent variables, the long-term follow-up outcome level (Level), initial change (IC) from baseline to 4 months postintervention, and subsequent change (SC) from 4 months to 15 months postintervention, the factor-loading matrix for each repeated-measures outcome shown in Figure 2 was specified
Thus, the observation y for individual i at Time 1, Time 2, and Time 3 can be expressed as
Therefore, the expected average at Time 3 indicates the long-term follow-up outcome level. The expected average changes from baseline to the 4-month assesssment and from the 4-month to the 15-month assessment are indicated by IC and SC, respectively (for greater detail, see Mun et al., in press).
Submitted: January 15, 2008 Revised: November 11, 2008 Accepted: November 17, 2008
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Source: Journal of Consulting and Clinical Psychology. Vol. 77. (1), Feb, 2009 pp. 88-102)
Accession Number: 2009-00563-017
Digital Object Identifier: 10.1037/a0014679
Record: 24- Title:
- Intrapersonal positive future thinking predicts repeat suicide attempts in hospital-treated suicide attempters.

- Authors:
- O'Connor, Rory C.. Suicidal Behavior Research Laboratory, Institute of Health & Wellbeing, University of Glasgow, Glasgow, United Kingdom, rory.oconnor@glasgow.ac.uk
Smyth, Roger. Department of Psychological Medicine, Royal Infirmary of Edinburgh, Edinburgh, Scotland
Williams, J. Mark G.. Department of Psychiatry, University of Oxford, Oxford, United Kingdom - Address:
- O'Connor, Rory C., Suicidal Behavior Research Laboratory, Institute of Health & Wellbeing, University of Glasgow, Glasgow, United Kingdom, G12 0XH, rory.oconnor@glasgow.ac.uk
- Source:
- Journal of Consulting and Clinical Psychology, Vol 83(1), Feb, 2015. pp. 169-176.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 8
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- suicidal, psychology, prospective, cognitive
- Abstract (English):
- Objective: Although there is clear evidence that low levels of positive future thinking (anticipation of positive experiences in the future) and hopelessness are associated with suicide risk, the relationship between the content of positive future thinking and suicidal behavior has yet to be investigated. This is the first study to determine whether the positive future thinking–suicide attempt relationship varies as a function of the content of the thoughts and whether positive future thinking predicts suicide attempts over time. Method: A total of 388 patients hospitalized following a suicide attempt completed a range of clinical and psychological measures (depression, hopelessness, suicidal ideation, suicidal intent and positive future thinking). Fifteen months later, a nationally linked database was used to determine who had been hospitalized again after a suicide attempt. Results: During follow-up, 25.6% of linked participants were readmitted to hospital following a suicide attempt. In univariate logistic regression analyses, previous suicide attempts, suicidal ideation, hopelessness, and depression—as well as low levels of achievement, low levels of financial positive future thoughts, and high levels of intrapersonal (thoughts about the individual and no one else) positive future thoughts predicted repeat suicide attempts. However, only previous suicide attempts, suicidal ideation, and high levels of intrapersonal positive future thinking were significant predictors in multivariate analyses. Discussion: Positive future thinking has predictive utility over time; however, the content of the thinking affects the direction and strength of the positive future thinking–suicidal behavior relationship. Future research is required to understand the mechanisms that link high levels of intrapersonal positive future thinking to suicide risk and how intrapersonal thinking should be targeted in treatment interventions. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Impact Statement:
- What is the public health significance of this article?—This study highlights the importance of positive future thinking as a predictor of future suicidal behavior. Clinicians ought to consider the content of positive future thinking, as not all types of positive future thinking are protective over time. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Attempted Suicide; *Hospital Admission; *Thinking; Future; Hopelessness; Hospitalized Patients; Major Depression; Suicidal Ideation
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Aged; Depressive Disorder; Female; Follow-Up Studies; Hope; Hospitalization; Humans; Male; Middle Aged; Risk; Self Concept; Self-Injurious Behavior; Suicidal Ideation; Suicide, Attempted; Thinking; Young Adult
- PsycINFO Classification:
- Behavior Disorders & Antisocial Behavior (3230)
- Population:
- Human
Male
Female
Inpatient - Location:
- Scotland
- Age Group:
- Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs)
Aged (65 yrs & older) - Tests & Measures:
- Positive Future Thinking Measure
Beck Depression Inventory DOI: 10.1037/t00741-000
Beck Hopelessness Scale
Scale for Suicide Ideation DOI: 10.1037/t01299-000 - Grant Sponsorship:
- Sponsor: Scottish Government, Chief Scientist Office, Scotland
Grant Number: CZH/4/449
Recipients: No recipient indicated
Sponsor: Wellcome Trust
Grant Number: GRO67797
Recipients: Williams, J. Mark G. - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Sep 1, 2014; Accepted: Jun 30, 2014; Revised: Jun 26, 2014; First Submitted: Nov 8, 2013
- Release Date:
- 20140901
- Correction Date:
- 20170223
- Copyright:
- The Author(s). 2014
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0037846
- PMID:
- 25181026
- Accession Number:
- 2014-36319-001
- Number of Citations in Source:
- 34
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-36319-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-36319-001&site=ehost-live">Intrapersonal positive future thinking predicts repeat suicide attempts in hospital-treated suicide attempters.</A>
- Database:
- PsycINFO
Intrapersonal Positive Future Thinking Predicts Repeat Suicide Attempts in Hospital-Treated Suicide Attempters
By: Rory C. O’Connor
Suicidal Behavior Research Laboratory, Institute of Health & Wellbeing, University of Glasgow;
Roger Smyth
Department of Psychological Medicine, Royal Infirmary of Edinburgh, Edinburgh, Scotland
J. Mark G. Williams
Department of Psychiatry, University of Oxford
Acknowledgement: We would like to thank information analyst Andrew Duffy, NHS National Services Scotland, for conducting the data extraction for the linkage component of the study. This research was supported by funding from the Chief Scientist Office, Scottish Government (CZH/4/449). Many thanks to Caoimhe Ryan who collected the data. J. Mark G. Williams is supported by the Wellcome Trust (Grant GRO67797).
Suicide and attempted suicide are major public health concerns, with approximately one million people dying by suicide annually across the globe (World Health Organization, n.d.). Indeed, as previous suicidal behavior is one of the strongest predictors of suicide (Hawton & van Heeringen, 2009), considerable research effort has been directed at understanding the etiology and course of suicide attempts. In recent years, there has also been increased recognition that we need to move beyond psychiatric categories and epidemiological risk factors to identify more specific markers of suicide risk (O’Connor & Nock, 2014; O’Connor, Smyth, Ferguson, Ryan, & Williams, 2013; van Heeringen, 2001). This has led to a concerted focus on basic science approaches to advance understanding of the psychological mechanisms that lead to suicidal behavior (e.g., Joiner, 2005; Nock et al., 2010; O’Connor, 2011; Van Orden et al., 2010; Williams, Barnhofer, Crane, & Beck, 2005).
One of the key advances has been the establishment of the link between hopelessness, defined as pessimism for the future, and suicide risk (O’Connor, Connery, & Cheyne, 2000; Petrie, Chamberlain, & Clarke, 1988; Beck, Steer, Kovacs, & Garrison, 1985). Hopelessness consistently predicts suicidal ideation and behavior (e.g., Brezo, Paris, & Turecki, 2006; Hawton, Saunders, & O’Connor, 2012). Although this bivariate relationship is robust, the work of MacLeod and others has demonstrated that hopelessness characterized by low levels of positive future thinking, rather than the preponderance of negative future thinking, is particularly important in the suicidal process (Hunter & O’Connor, 2003; MacLeod, Pankhania, Lee, & Mitchell, 1997; MacLeod et al., 1998; O’Connor, Fraser, Whyte, MacHale, & Masterton, 2008). Positive future thinking, defined as anticipation of positive experiences in the future, is usually assessed via the future thinking task (MacLeod et al., 1997), during which participants are asked to generate as many future events or experiences as possible that they are looking forward to.
Evidence from both clinical and nonclinical populations and from different research groups is consistent: Low levels of positive future thinking (i.e., few positive future thoughts) are associated with suicidality independent of depression, verbal fluency, and negative attributional style (Hunter & O’Connor, 2003; MacLeod et al., 1997; O’Connor, Connery, & Cheyne, 2000; Williams, Van der Does, Barnhofer, Crane, & Segal, 2008). This finding is clinically important, as positive future thinking provides targets for treatment intervention; theoretically, it is noteworthy as future plans and goals are key components of predominant models of suicidal behavior (O’Connor, 2011; Williams, 2001) as well as self-regulatory theories of wellbeing (Carver & Scheier, 1998).
Despite the accumulation of evidence in support of the positive future thinking–suicidality relationship, there are a number of key questions about the nature of this relationship that have yet to be addressed. First, does positive future thinking predict suicide-related outcomes over the medium to long term? To date, there is no evidence that low levels of positive future thinking have predictive validity beyond the first 2 to 3 months following an index suicide attempt. In the only clinical study of its kind, O’Connor et al. (2008) found that low levels of positive future thinking were better predictors of suicidal ideation than global hopelessness 2 to 3 months following a suicide attempt. To our knowledge, no other longer term studies of suicidal individuals have been conducted, and no previous study has investigated whether positive future thinking predicts actual suicidal behavior over time.
Second, it is unclear whether all types of positive future thinking are protective against suicidal behavior. The studies thus far have focused on establishing the presence of a relationship between the frequency of positive future thinking or the likelihood of these future events occurring and suicidality. None of the previous studies had been set up to investigate whether the content of positive future thinking affects the relationship between positive future thinking and suicidality. It is reasonable to posit, for example, that positive future thinking focused on changing a personal attribute (for the better) may not be protective if it is not possible to realize this change over time. Arguably, trait-like intrapersonal characteristics (e.g., being more confident, optimistic) may fall into this category. It may be, therefore, that high levels of such thinking are problematic in some circumstances. According to the integrated motivational–volitional model of suicidal behavior (IMV; O’Connor, 2011), such thinking, if experienced contemporaneously with feelings of entrapment (defined as the inability to escape from defeating or stressful circumstances, Gilbert & Allan, 1998; Williams, 2001), would increase the likelihood of suicidal thoughts developing. It is the thwarted motivation to escape that distinguishes entrapment from hopelessness, and it is posited that as entrapment increases (and no solutions are found) the likelihood that suicide will be considered as an escape strategy also increases (Gilbert & Allan, 1998; O’Connor et al., 2013; Taylor, Gooding, Wood, & Tarrier, 2011).
To address the former question directly, we modified an existing coding frame for positive future thinking (Godley, Tchanturia, MacLeod, & Schmidt, 2001) and classified the content of positive future thinking from a large sample of suicide attempters into seven categories. Using linkage methodology, we were able to investigate (a) whether positive future thinking predicts repeat suicidal behavior up to 15 months following an index suicide attempt (beyond the effects of traditional clinical risk factors), and (b) whether the content of positive future thinking affects the relationship between positive future thinking and repeat suicidal behavior.
Method Participants and Procedure
We recruited 388 patients who were seen by the liaison psychiatry service the morning after presenting at a single general hospital in Edinburgh, Scotland, following a suicide attempt between January 2008 and September 2009. The hospital provides a full range of acute medical and surgical services, including an accident and emergency service. The vast majority of patients had presented with overdose (93%, n = 361). Exclusions were limited to participants who were unfit to participate (e.g., actively psychotic), who were unable to give informed consent (e.g., medically unfit to give informed consent), who were participating in one of the other studies being conducted in the hospital, or who were unable to understand English. Approximately 10% of participants who were approached declined to take part (10.2%, N = 44). There were 220 females and 168 males, with an overall mean age of 35.3 years (SD = 13.91, range = 16–71 years). The men (M = 38.40, SD = 14.04) and women (M = 32.92, SD = 13.36) did not differ significantly in age, t(386) = 3.92, ns. Ethnicity was not recorded.
Baseline data were collected in hospital, usually within 24 hr of admission. The Information Services Division of the National Health Service Scotland maintains a national database of hospital records and mortality data. This nationally linked database is a powerful resource, as it allows us to determine whether a patient is readmitted to hospital in Scotland with self-harm at any time since their index episode. We asked the Information Services Division to extract hospital admissions for self-harm in the period between the index self-harm episode and 15 months later for each patient. We also reviewed the electronic medical records of those patients who were hospitalized again following self-harm during the follow-up period to determine whether the repeat self-harm episode was a suicide attempt or not.
Participants completed the following measures in hospital.
Baseline Measures
Positive future thinking
Positive future thinking was recorded via the future thinking task (MacLeod et al., 1997). This requires participants to think of potential future experiences that they are looking forward to across three time periods: the next week (including today), the next year, and the next 5 to 10 years. On each occasion, participants have 1 min to think of future experiences for a given time period; this is repeated until all three periods are assessed. Before administration of the future thinking task, all participants complete the standard verbal fluency task (to control for general cognitive fluency) in which they have to generate as many words as possible to three letters (F, A, S), with 1 min allowed per letter. Consistent with previous research (MacLeod et al., 1997), the time periods are aggregated to yield total positive future thinking scores (i.e., the total number of positive future thoughts per participant). The contents of positive future thinking were coded according to a modified version of Godley et al.’s (2001) coding frame for positive future thinking to yield a total number of positive future thoughts per category (see Table 1). There were seven categories of positive future thinking. Social/interpersonal relates to positive future thinking that involves at least one other person (e.g., marriage). Achievement relates to the anticipation of any achievement-related event (e.g., new job). Intrapersonal thinking is any thought that concerns the individual and no-one else (e.g., being happy). Leisure/pleasure refers to any event or activity that is undertaken for leisure or pleasure (e.g., going on holiday). Health of others (e.g., mother getting better) and financial and home (e.g., decorating the house) describe thinking that concerns improvement in the health of family or friends and any aspect of finance or home, respectively. The final category, other, describes any thinking that does not fit into the preceding categories. Three raters independently rated 15% of the responses and agreement was good (κ = .83, .90, .85 for raters 1 + 2, 1 + 3, and 2 + 3, respectively). All of the responses were then categorized by the first coder.
Coding System for the Content of Positive Future Thinking and Mean Number of Thoughts as a Function of a Suicide Attempt or Suicide During Follow-Up
Depression
The Beck Depression Inventory (Beck, Steer, & Brown, 1996) is a well-established measure of depressive symptomatology. It consists of 21 groups of statements that assess the presence of depressive symptoms in the past 2 weeks with good reliability and validity. Cronbach’s α was .91.
Hopelessness
Hopelessness was measured using the 20-item Beck Hopelessness Scale. This is reliable and valid and has been shown to predict eventual suicide (Beck, Schuyler, & Herman, 1974; Beck, Steer, Kovacs, & Garrison, 1985). In the present study, internal consistency was very good (Kuder-Richardson–20 = .92).
Suicidal ideation
Participants’ thoughts of suicide over the past week were assessed via the 21-item Scale for Suicide Ideation (SSI; Beck, Steer, & Ranieri, 1988; Beck, Steer, & Brown, 1996). Cronbach’s α was .94.
Suicide intent
Suicide intent was assessed via the SSI (Beck et al., 1974). The SSI consists of 15 items that assess the objective circumstances related to a suicide attempt (eight items) and self-reported beliefs about one’s intention (seven items). Cronbach’s α was .72.
Outcome Measure
Readmission to hospital with a suicide attempt
An episode of self-harm was recorded if a patient was admitted to any hospital in Scotland with self-harm in the 15 months following the index episode. For this data set, the Information Services Division successfully linked 96.4% of the sample (n = 374/388). Where a patient was readmitted to hospital with self-harm during the study period, we reviewed their medical records to ascertain whether this episode was a suicide attempt or not. We were able to determine the presence/absence of suicidal intent in 93.1% (94/101) of those who were admitted to hospital with self-harm again during the study period. Therefore, all analyses are based on the 367 participants who were linked and for whom we have suicide intent data if they were readmitted to hospital with self-harm (which represents 95% of the original sample). Two trained coders independently rated the extracts from the medical records and agreed on all cases. Coders were unaware of any of the baseline measures.
Statistical analyses
We conducted a series of univariate logistic regression analyses for each predictor of a future suicide attempt. The total number of positive future thoughts per category is entered into the regression analyses. Although we are interested specifically in the positive future thinking logistic regression analyses, we present the findings for other established predictors of suicidal behavior (i.e., depression, hopelessness, suicide ideation, past suicide attempts). To test the two hypotheses, we also conducted multivariate logistic regression analyses including all significant univariate predictors, as appropriate.
Results Linked Sample
There were 208 women and 159 men with an overall mean age of 35 years (SD = 13.7, range: 16–71 years) in the linked sample. At baseline, 39.0% of participants (n = 143) reported no previous suicide attempts, 24.0% of participants reported one previous attempt (n = 88), 8.7% reported two previous attempts (n = 32) and 28.3% reported three or more previous episodes (n = 104). As anticipated, all indices of psychological distress were positively correlated (see Table 2). For the most part, the different categories of positive future thinking were negatively correlated with depression, hopelessness, and suicidal ideation. Suicidal intent was negatively correlated with two of the positive future thinking categories (interpersonal and achievement positive future thinking), as well as positively correlated with the psychological distress indicators. Finally, more previous suicide attempts were associated with increased distress and less interpersonal, achievement and leisure/pleasure positive future thinking.
Correlations, Means, and Standard Deviations for All of the Study Variables for All Participants
Individual and Multivariate Predictors of Repeat Suicide Attempts
Between Time 1 and Time 2 (15 months after the index episode), 25.6% (n = 94) of the linked participants either were readmitted to hospital with a suicide attempt or died by suicide (5/94). We conducted a series of logistic regression analyses to determine the variables for entry into the multivariate analyses. Established correlates of suicidal behavior (e.g., depression, suicidal ideation) were included in the analyses to ensure a robust test of the positive future thinking–repeat suicide attempt relationship. None of the demographic variables were significant univariate predictors of repeat suicide attempts (see Table 3). However, among the clinical predictors, the number of previous suicide attempts, suicidal ideation, hopelessness, and depression emerged as significant predictors. In respect to positive future thinking, low levels of achievement and financial positive future thinking were associated with suicide attempts between Time 1 and Time 2, whereas high levels of intrapersonal positive future thinking was also significant (see Table 3).
Univariate Associations Between Predictors and Suicide Attempts or Suicide Between Time 1 and Time 2
To investigate whether positive future thinking has utility in predicting repeat suicidal behavior up to 15 months following an index suicide attempt (beyond the effects of traditional clinical risk factors) and whether the content of positive future thinking affects the relationship between positive future thinking and repeat suicidal behavior, the significant univariate predictors were entered into the multivariate logistic regression in two stages. The traditional clinical risk factors were entered at Step 1, followed by the positive future thinking variables at Step 2. As is evident in Table 4, intrapersonal positive future thinking is a significant predictor of repeat suicide attempts in the final model (OR = 1.25, 95% CI [1.07, 1.44]), and its inclusion adds incremental predictive validity over previous suicide attempts and suicidal ideation (χ2 = 11.34, p < .01).
Multivariate Logistic Regression Analysis to Predict Suicide Attempts or Suicide Between Time 1 and Time 2
DiscussionThe present study extends understanding of the relationship between positive future thinking and suicide attempts. First, the findings demonstrate that some intrapersonal positive future thoughts predict repeat suicidal behavior up to 15 months following an index suicide attempt. Second, they also show that the relationship between positive future thinking and suicidality varies as a function of the content of such thinking. Specifically, in the univariate analyses, high levels of intrapersonal positive future thinking were associated with the risk of repetition, whereas low levels of achievement and financial positive future thinking were associated with repeat suicidal behavior. What is more, the multivariate analyses suggest that intrapersonal positive future thinking is most pernicious of all, as the effects of achievement and financial future thinking were rendered nonsignificant when considered alongside past suicidal behavior, suicidal ideation, hopelessness, and depression.
The findings are also noteworthy because they highlight not only that the types of positive future thinking have differential predictive validity but crucially because they show that high levels of positive future thinking are not always protective. On the face of it, this may seem counterintuitive, given the generally accepted view that high levels of positive thinking buffer against distress in the face of life stress (e.g., O’Connor, O’Connor, O’Connor, Smallwood, & Miles, 2004). Moreover, closer inspection of the baseline correlations shows that the degree of protection also changes as a function of the individual’s current context. When participants are in crisis, in the hours following a suicide attempt, high levels of intrapersonal positive future thinking appear to be protective, as illustrated by the negative correlations between intrapersonal future thinking, hopelessness, and suicidal ideation. These baseline findings are consonant with the extant literature on positive future thinking, which has consistently demonstrated that suicidal individuals generate lower levels of positive future thinking than controls (e.g., Hunter & O’Connor, 2003; MacLeod et al., 1997).
However, over the subsequent 15 months, the reverse relationship is apparent. The likelihood of another suicide attempt was elevated among those who reported more intrapersonal positive future thinking at baseline (when in crisis). As noted in the introduction, one possible explanation for the latter relationship may be that, over time, participants develop beliefs that their intrapersonal future thoughts are not attainable as they have not been able to achieve what they had expected within the intrapersonal domain over the duration of the study. It may be that these beliefs exacerbate their sense of entrapment, thereby increasing their risk of repeat suicidal behavior. Alternatively, it may simply be that the generation of positive future thinking is confounded by contemporaneous mood effects. The latter is unlikely, however, as baseline mood was controlled for in the multivariate analyses. Nonetheless, the unachievability hypothesis requires closer scrutiny in future research, as entrapment was not assessed in the present study, and assessing the impact of mood on intrapersonal versus external positive future thinking requires a specific test in which mood is experimentally manipulated. A further competing hypothesis is that frequent swings in self-image from high to low and vice versa that characterize some clients’ cognitions (e.g., clients with borderline personality or bipolar disorder) may account for the present findings. As we only assessed positive future thinking at one time point (and we also did not assess clinical disorder), it was not possible to test this hypothesis directly. Therefore, future research should investigate whether this instability in cognition has explanatory power in the present context.
Two other methodological points also merit comment. The first point relates to the test–retest reliability of the positive future thinking task. To our knowledge, this has not been formally tested; however, evidence from a recent experimental study in which positive future thinking was assessed twice within a single testing session suggests that responses are stable in the very short term (O’Connor & Williams, 2014). However, it is important to investigate this issue further to tease out whether, for example, intrapersonal positive future thinking is highly unstable when assessed over a period of days and weeks rather than hours.
Another issue relates to the extent to which the positive future thinking task is useful outside of the 24 hr following a suicide attempt. Although most studies have administered it within this time frame, other studies have employed it within 7 days of a suicidal episode (MacLeod et al., 2005), and others still have employed it in healthy populations (O’Connor & Williams, 2014; Williams et al., 2008) and found the expected relationships with hopelessness and suicidal ideation. Given this evidence, we do not think that the findings reported here are circumscribed to the immediate post-suicide-attempt period. Indeed, it is likely that the pattern of positive future thinking found in the perisuicidal period is similar to that found in the post-suicide-attempt period, but this is an empirical question. Indeed, it is critical that future research explore the trajectory of positive future thinking over time, to better understand the dynamic relationship between the levels of positive future thinking and suicide risk before, during, and after crisis.
Implications
Irrespective of the mechanism(s) of effect, the preeminence of intrapersonal rather than other dimensions of positive future thinking, including interpersonal thoughts, is clear from the present findings, as the former was the only category of positive future thinking to emerge from the multivariate analyses. This pattern of findings is also consistent with the integrated motivational–volitional model of suicidal behavior (O’Connor, 2011), which argues that positive future thinking may increase the likelihood that suicidality emerges from entrapment beliefs. Furthermore, the present findings have implications for how to intervene effectively with those who have attempted suicide to reduce risk of repetition. They clearly suggest that the content of positive future thinking requires careful consideration as part of the formulation process. Indeed, it may be helpful to monitor the achievability or otherwise of intrapersonal positive future thinking and to develop strategies to maximize the likelihood that the intrapersonal expectations are attainable. Patients may also benefit from help with problem solving when the expectations are not realized. Alternatively, in situations where the expectations are unrealistic or unattainable (e.g., O’Connor, O’Carroll, Ryan, & Smyth, 2012; Wrosch, 2010), working collaboratively with the individual to disengage from such future expectations in a safe manner and engage with new, more realistic positive future thinking may bear fruit.
There are also a number of research implications. First, positive future thinking is treated as a continuous variable in the present study. It would be useful, therefore, to investigate whether there is a critical threshold at which positive future thinking becomes especially deleterious, but this is likely best achieved by also assessing the perceived achievability of the positive future thinking. It would also be useful to investigate an individual’s certainty about a positive event occurring in the future and how this relates to risk (Sargalska, Miranda, & Marroquin, 2011). Second, for pragmatic reasons we employed cognitive assessment to record current psychological state rather than conducting a formal clinical assessment. It may be helpful in the future, therefore, to investigate whether the relationship between positive future thinking and suicide risk varies as a function of clinical diagnostic category. Third, whereas we coded the contents of individuals’ thinking post hoc, it would be interesting to ask participants to generate specific types of positive future thinking to determine whether there are different ways in which individuals rate their own thinking. More generally, the findings highlight the utility of focusing on psychological processes to identify more specific markers of suicide risk (O’Connor & Nock, 2014).
Although the longitudinal design and the use of an objective outcome measure are notable strengths of the present study, there are a number of potential limitations that merit comment. First, as we concentrated on hospital admissions and mortality, the present study was not designed to capture less medically serious suicide attempts that did not come to the attention of clinical services. In addition, although unlikely, we also would have missed any hospitalizations or deaths that occurred outside Scotland. Also, the national linkage methodology did not record those individuals who presented to the emergency department but were subsequently discharged without hospitalization. Consequently, future research is required to determine whether a similar pattern of findings would hold for non-medically serious suicide attempts. It would also be useful to look at nonsuicidal self-injury as another self-destructive outcome variable. Finally, as all of the participants had attempted suicide at baseline, it is unclear whether high levels of intrapersonal positive future thinking predict a first episode suicide attempt.
ConclusionsThis is the first study to investigate whether the content of positive future thinking predicts repeat suicidal behavior over the medium term. The findings demonstrate clearly that the content of the thoughts affects the direction and strength of the positive future thinking–suicidal behavior relationship. Whereas previous research had shown that low levels of positive future thinking are associated with suicidal behavior, the present study found that high levels of intrapersonal positive future thoughts predict repeat suicide attempts over time. Future research is required to understand the mechanisms that link intrapersonal positive future thinking to suicide risk and how intrapersonal positive future thinking should be targeted in treatment interventions.
Footnotes 1 As general verbal fluency did not predict repeat suicide attempts (OR = .98, 95% CI [0.94, 1.01]), it was not considered any further in the main analyses.
2 It is worth highlighting that none of the earlier studies analyzed positive future thinking as a function of thought content.
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Submitted: November 8, 2013 Revised: June 26, 2014 Accepted: June 30, 2014
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Source: Journal of Consulting and Clinical Psychology. Vol. 83. (1), Feb, 2015 pp. 169-176)
Accession Number: 2014-36319-001
Digital Object Identifier: 10.1037/a0037846
Record: 25- Title:
- Intrusive memories in perpetrators of violent crime: Emotions and cognitions.
- Authors:
- Evans, Ceri. Department of Psychological Medicine, St. George's Hospital Medical School, London, England, ceri.evans@cdhb.govt.nz
Ehlers, Anke, ORCID 0000-0002-8742-0192. Department of Psychology, Institute of Psychiatry, King's College, London, England
Mezey, Gillian. Department of Psychological Medicine, St. George's Hospital Medical School, London, England
Clark, David M., ORCID 0000-0002-8173-6022. Department of Psychology, Institute of Psychiatry, King's College, London, England - Address:
- Evans, Ceri, Medlicott Academic Unit of Forensic Psychiatry, Forensic Psychiatry Services, Hillmorton Hospital, Private Bag 4733, Christchurch, New Zealand, ceri.evans@cdhb.govt.nz
- Source:
- Journal of Consulting and Clinical Psychology, Vol 75(1), Feb, 2007. pp. 134-144.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- perpetrators, violent crime, intrusive memories, posttraumatic stress disorder, dissociation, emotions, cognitions
- Abstract:
- The authors investigated factors that may determine whether perpetrators of violent crime develop intrusive memories of their offense. Of 105 young offenders who were convicted of killing or seriously harming others, 46% reported distressing intrusive memories, and 6% had posttraumatic stress disorder. Intrusions were associated with lower antisocial beliefs before the assault, greater helplessness, fear, dissociation, data-driven processing and lack of self-referent processing during the assault, more disorganized assault narratives, and greater negative view of the self, negative interpretations of intrusive memories, perceived permanent change, and self-blame. In a logistic regression analysis, the cognitive and emotional variables explained substantial variance over and above demographic factors. The results suggest that cognitive factors that predict reexperiencing symptoms in victims of crime generalize to perpetrators. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Dissociation; *Memory; *Perpetrators; *Posttraumatic Stress Disorder; *Violent Crime; Cognitive Processes; Emotional States
- Medical Subject Headings (MeSH):
- Adult; Affect; Cognition; Crime; Dissociative Disorders; Humans; Memory; Prevalence; Stress Disorders, Post-Traumatic; Surveys and Questionnaires; Violence
- PsycINFO Classification:
- Criminal Behavior & Juvenile Delinquency (3236)
- Population:
- Human
Male - Location:
- United Kingdom
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Index Offence Interview
Intrusion Interview
Posttraumatic Stress Scale-Interview version
Perceived Physical Threat Scale
Emotions During the Assault Scale
Negative View of the Self Scale
Self-Blame Scale
Data-Driven Processing scale
Posttraumatic Diagnostic Scale DOI: 10.1037/t02485-000
Antisocial Beliefs Scale DOI: 10.1037/t08054-000
Lack of Self-Referent Processing Scale DOI: 10.1037/t08235-000
Perceived Social Image Damage Scale DOI: 10.1037/t08261-000
Peritraumatic Dissociative Experiences Questionnaire—Rater Version DOI: 10.1037/t02464-000 - Grant Sponsorship:
- Sponsor: Wellcome Trust
Other Details: Principal Research Fellowship
Recipients: Ehlers, Anke - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Jul 20, 2006; Revised: Jul 18, 2006; First Submitted: Nov 28, 2005
- Release Date:
- 20070212
- Correction Date:
- 20120827
- Copyright:
- American Psychological Association. 2007
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/0022-006X.75.1.134
- PMID:
- 17295572
- Accession Number:
- 2007-00916-014
- Number of Citations in Source:
- 49
- Persistent link to this record (Permalink):
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- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2007-00916-014&site=ehost-live">Intrusive memories in perpetrators of violent crime: Emotions and cognitions.</A>
- Database:
- PsycINFO
Intrusive Memories in Perpetrators of Violent Crime: Emotions and Cognitions
By: Ceri Evans
Department of Psychological Medicine, St. George's Hospital Medical School, London, England;
Anke Ehlers
Department of Psychology, Institute of Psychiatry, King's College, London, England
Gillian Mezey
Department of Psychological Medicine, St. George's Hospital Medical School, London, England
David M. Clark
Department of Psychology, Institute of Psychiatry, King's College, London, England
Acknowledgement: The present study was supported by a Wellcome Trust Principal Research Fellowship, awarded to Anke Ehlers.
Recent studies have suggested that a minority of perpetrators of violent crime may develop posttraumatic stress disorder (PTSD; Kruppa, Hickey, & Hubbard, 1995; Spitzer et al., 2001), but little is known about the conditions that may turn an intentional violent act into a trauma for the perpetrator. Clinical examples include exposure to the gruesome consequences of violence (e.g., victim's body covered in blood), unintended seriousness of the consequences of the violence (e.g., victim died, although the perpetrator did not intend to kill him or her), or greater violence than intended under social pressure (e.g., as part of gang violence; Evans, Ehlers, Mezey, & Clark, in press).
The present article was designed to systematically investigate factors that may lead to perpetrators' intrusive memories of violent crime. As reexperiencing symptoms are the hallmark symptom of PTSD (Horowitz, 1976), studying the factors that lead to intrusive memories is a crucial step in understanding how PTSD may develop in perpetrators. Previous theoretical and empirical work identified the following factors in the etiology of intrusive memories after trauma: (a) cognitive schemas (beliefs, appraisals) before and after the assault, (b) perceived threat to life, (c) overwhelming negative emotions, and (d) disrupted cognitive processing, leading to problems with the autobiographical memory for the trauma (Brewin, Dalgleish, & Joseph, 1996; Ehlers & Clark, 2000; Horowitz, 1976; van der Kolk & Fisler, 1995). In the present study, we investigated whether these factors apply to perpetrators of violent crime.
Cognitive Schemas Before the TraumaThe development of intrusive memories in survivors of trauma has been attributed to a shattering of their pretrauma beliefs about safety, personal vulnerability, and the predictability of the future (Foa & Riggs, 1993; Janoff-Bulman, 1992; Resick & Schnicke, 1993). One would thus expect that perpetrators with antisocial personality disorder—who hold beliefs such as “I am entitled to break rules to look after myself” or “Force or cunning is the best way to get things done” (Beck, Freeman, & Associates, 1990)—to be at low risk of developing intrusions of their crimes.
Perceived Threat and Negative Emotions During TraumaThe exceptionally threatening character of traumatic events has been highlighted in the diagnostic criteria for PTSD (American Psychiatric Association, 1980; World Health Organization, 1992). Perceived threat to life during trauma showed consistent correlations with PTSD severity in a recent meta-analysis (Ozer, Best, Lipsey, & Weiss, 2003), with an average weighted correlation of .26. For perpetrators of violence, the perceived threat to their social status may be an important additional source of threat (Beck, 1999). It was therefore included as a possible predictor in the present study.
Emotional reactions during trauma are also highlighted in the diagnostic criteria for PTSD, in particular fear, helplessness, or horror (American Psychiatric Association, 1994). In Ozer et al.'s (2003) meta-analysis, the intensity of such negative emotions showed an average weighted correlation of .26 with PTSD severity. Other negative emotions that have been shown to predict PTSD include anger and shame (Andrews, Brewin, Rose, & Kirk, 2000).
Cognitive Processing and Disorganized Trauma MemoriesTheories of PTSD suggest that information processing is compromised during trauma and that compromised information processing explains PTSD symptom severity over and above what is explained by high arousal and negative emotions (e.g., Brewin et al., 1996; Ehlers & Clark, 2000). The most widely investigated indicator of such compromised processing is dissociation, which was the best predictor of PTSD in Ozer et al.'s (2003) meta-analysis, with an average weighted correlation of .35.
Dissociation is a complex concept, and it is unclear how it relates to other forms of cognitive processing that have been shown to influence memory (Roediger, 1990; Wheeler, 1997, 2000). Ehlers and Clark (2000) suggested that two further cognitive processing dimensions, data-driven processing (i.e., the predominant processing of sensory as opposed to conceptual information) and lack of self-referent processing (i.e., failure to encode new information as related to the self and other autobiographical information), predict whether people develop reexperiencing symptoms after trauma. These processes are thought to overlap in part with aspects of dissociation. Preliminary empirical support for a role of data-driven processing and lack of self-referent processing in intrusive trauma memories was found in studies of trauma survivors and volunteers exposed to distressing films (Murray, Ehlers, & Mayou, 2002; Rosario, Williams, & Ehlers, 2006).
Compromised cognitive processing is thought to lead to deficits in the autobiographical memory for the traumatic event. There are different hypotheses about the nature of this deficit, including a deficit in memory representations that facilitate intentional recall (Brewin et al., 1996), highly fragmented memories (e.g., Foa & Riggs, 1993; Herman, 1992), and poorly elaborated memories that are inadequately incorporated into their context of other autobiographical memories (e.g., Ehlers & Clark, 2000). Poor elaboration is thought to lead to poor inhibition of unintentional triggering of aspects of the trauma memory by matching cues. Ehlers, Hackmann, and Michael (2004) further suggested that the poor elaboration should be most pronounced for those parts of the trauma that are later reexperienced.
The mechanisms involved with the formation of trauma memories and deficits in recall specified in the different PTSD models are difficult to measure (Ehlers et al., 2004; McNally, 2003). One way is to code narratives of the traumatic event for indicators of the hypothesized mechanism. Common to the fragmentation and poor elaboration models is the hypothesis that intentional recall of trauma memories should be disorganized. Several studies have shown preliminary support for more disorganized trauma narratives in patients with PTSD versus those without PTSD (Foa, Molnar, & Cashman, 1995; Halligan, Michael, Clark, & Ehlers, 2003; Murray et al., 2002) and in volunteers exposed to a highly unpleasant film who developed intrusive memories than those without subsequent intrusions (Halligan, Clark, & Ehlers, 2002).
Appraisals of the Trauma and Its AftermathPTSD has been found to be associated with excessively negative appraisals of traumatic events (Ehlers & Clark, 2000; Foa & Riggs, 1993; Resick & Schnicke, 1993). For example, trauma survivors who blame themselves for the event or those who appraise a traumatic event as a sign of a negative (e.g., incompetent, unworthy, inadequate) self have more persistent PTSD symptoms than those who do not (Andrews et al., 2000; Dunmore, Clark, & Ehlers, 1997, 1999, 2001; Ehlers, Maercker, & Boos, 2000; Foa, Tolin, Ehlers, Clark, & Orsillo, 1999).
Although it is common for people to experience temporary unwanted memories following trauma, only a subgroup suffer from persisting intrusive memories (e.g., Baum & Hall, 1993). Ehlers and Steil (1995) suggested that negative interpretations of intrusions and other PTSD symptoms contribute to the maintenance of intrusive memories because they motivate the survivor to engage in behaviors that prevent processing of the trauma and may even increase intrusion frequency (e.g., rumination, thought suppression, use of alcohol and drugs). Several studies have supported the role of negative interpretations of intrusions in maintaining intrusions and PTSD (e.g., Dunmore et al., 1999, 2001; Ehlers, Mayou, & Bryant, 1998). Other trauma sequelae may also be interpreted in a negative way, contributing to the maintenance of PTSD (Ehlers & Clark, 2000). A common example is that trauma survivors interpret the trauma and its consequences as meaning that they have permanently changed for the worse as a person. Perceived permanent change has been shown to predict chronic PTSD (Dunmore et al., 1999, 2001; Ehlers et al., 2000).
Study Aims and HypothesesWe investigated the relationship between emotional and cognitive factors and intrusive memories in perpetrators of violent crime. On the basis of prior research and theories of PTSD, we expected that intrusive memories would be associated with (a) low prior antisocial beliefs; (b) threat perception during the assault; (c) negative emotions during the assault; (d) dissociative, data-driven, and lack of self-referential cognitive processing during the assault, (e) disorganization of the assault narrative; and (f) negative appraisals of the assault and/or its aftermath. We also expected these variables to be associated with PTSD symptom severity. In addition, we explored Ehlers et al.'s (2004) hypothesis that problems in intentional recall in PTSD are greatest for the moments of the trauma that are reexperienced.
Method Participants
Participants were 105 male prisoners, all of whom had been convicted of grievous bodily harm (GBH), attempted murder, manslaughter, or murder. All participants were imprisoned at two young offenders institutions (YOIs) within the United Kingdom during the 20-month study period. The exclusion criteria were (a) unable to speak English fluently, (b) severe learning disability, (c) active psychosis, (d) actively suicidal, (e) denied being present at the scene of the offense, and (f) unacceptably high security risk (e.g., a history of hostage taking). Of the 149 prisoners who met the legally defined entry criteria during the study period, 113 were suitable for inclusion in the study. All suitable prisoners were invited to take part. Of these, 6 (5%) declined to participate without stating a reason, and 2 (2%) refused because they experienced distressing flashbacks during the consenting process, giving an overall compliance rate of 105 out of 113 participants approached (93%). All participants completed the study measures.
Measures
Demographic characteristics were assessed using a semistructured interview, adapted for perpetrators from Dunmore et al. (1999, 2001). It included questions relating to demographic information, history of treatment for a psychiatric disorder, and history of a previous violent offense. Previous traumatic experiences were assessed with the trauma checklist from the first part of the Posttraumatic Diagnostic Scale (Foa, Cashman, Jaycox, & Perry, 1997).
Characteristics of the offense were assessed using The Index Offence Interview, a semistructured interview adapted for perpetrators from Dunmore et al. (1999, 2001). It included questions related to (a) legal aspects (e.g., conviction, plea, initial charge, sentence), (b) descriptive aspects (e.g., victim[s], location, timing, duration, use of weapons), (c) medical aspects (e.g., victim and perpetrator injuries), and (d) situational aspects (e.g., drug or alcohol intoxication, background stress, perceived provocation, planning and preparation, motivation for the assault, including intent to kill the victim).
Measures of Intrusions and PTSD Symptoms
Intrusion interview
The presence or absence of intrusive memories for the index offense was assessed using the Intrusion Interview (Michael, Ehlers, Halligan, & Clark, 2005), a semistructured 30-min interview that covers occurrence, content, frequency, modalities, and qualities of intrusive memories. Intrusive memories were defined as memories that (a) were part of what actually happened at the time and (b) were recurrent, distressing, and involuntarily triggered. The interviewer first asked a generic screening question designed to elicit reports of unwanted memories of the assault of an intrusive nature:
People who have committed a violent offence [sic] can remember the event in different ways. Some people have memories of parts of the assault that just pop into their mind when they do not want them to. These are usually from particular moments from before, during or after the incident that somehow “got stuck” in memory and keep coming back. These memories consist of part of what actually happened at the time, rather than your thoughts about what has happened since, such as being in prison because of the assault. Do you sometimes get such unwanted recollections of the assault?
If endorsed, then participants were asked to describe all such intrusive memories in detail. If more than one intrusive memory was identified, then the participant was asked to identify the one that was most upsetting or distressing and to describe this intrusion in greater detail. Examples of the intrusions included images of the wounded victim (e.g., “I get the picture of his face in my head… I can see blood coming out the back of his head… I thought he was dead”), or intrusions of the sensations accompanying the weapon causing damage to the victim (e.g., “The knife goes in and I see… blood squirt out… you know, you get that smell of blood… and the squirt… its just like the smell of blood. A lot of blood… a kind of 'iron-ey' kind of smell… I hear the squirt of the blood.”).
Interviews were transcribed verbatim. Two raters independently rated the transcripts of the intrusion interviews to determine whether intrusive memories reported by the participant met criteria for an intrusive memory. The interrater reliability was high (κ = 0.90, p < .001, N = 105). Discussion between the two raters led to resolution of all five cases involving disagreement. A previous study showed that the 1-week test–retest reliability of the interview scales ranged between r = .61 and r = .72 (Speckens, Ehlers, Hackmann, Ruths, & Clark, 2006).
The Posttraumatic Stress Scale–Interview version (PSS-I; Foa, Riggs, Dancu, & Rothbaum, 1993)
The PSS-I is a 17-item structured interview that assessed current symptoms of PTSD in relation to the index offense as defined by the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM–IV;American Psychiatric Association, 1994). The interviewer rates each symptom on a scale ranging from 0 (not at all) to 3 (five or more times per week/very much). The total PSS-I score is the sum of the ratings for the 17 items. The scale has high internal consistency (α = .85), moderate to high correlations with other measures of psychopathology, high test–retest reliability (r = .80), high interrater reliability (κ = 0.91), and good diagnostic agreement with the Structured Clinical Interview for DSM (Foa et al., 1993) and the Clinician-Administered PTSD Scale (Foa & Tolin, 2000). In order to qualify for a diagnosis of PTSD, participants had to have the minimum number of symptoms specified in the DSM–IV, scored with at least 1 (once per week or less/a little).
Measures of Predictor Variables
If not mentioned otherwise, participants rated their agreement with each item of the following questionnaires on a 7-point scale ranging from 1 (strongly disagree) to 7 (strongly agree).
Antisocial Beliefs Scale
This questionnaire was developed for the purposes of the present study to assess antisocial beliefs prior to the offense, using typical antisocial beliefs listed in Beck et al. (1990; six items, e.g., “force or cunning is the best way to get things done”; α = .85). Participants were instructed to answer the scale items with respect to their beliefs before the index offense.
The Perceived Physical Threat Scale (Dunmore et al., 1997)
This measure was used to ask participants about the extent to which he believed he would be seriously injured at the time of the assault (two items such as “During the assault I believed that I would be seriously injured”; α = .77).
The Perceived Social Image Damage Scale
This measure was developed for the purposes of the present study and assessed the extent to which the participant felt diminished as a result of the victim's actions immediately before the assault, particularly with respect to undermining the image that he perceived that others held of him. The items were based on Beck's (1999) categories of social transgressions, which can lead to perceived psychological injury or damaged personal self-esteem (12 items such as “The victim's actions caused me to lose face”; α = 85).
Emotions During the Assault Scale (Dunmore et al., 1999)
Participants rated the extent to which they experienced each of a list of 23 emotions during the assault on a 5-point scale ranging from 0 (not at all) to 4 (very strongly). A principal-axis factor analysis with oblimin rotation extracted six factors with eigenvalues greater than 1.00. The four scales reflecting negative emotions were interpreted as Helpless (four items: helpless, sad, betrayed, inferior; α = .73), Anger (five items: angry, furious, frustrated, hatred, insulted; α = .83), Shame (two items: ashamed, embarrassed; α = .85), and Fear (two items: terrified, afraid; α = .90).
The Negative View of the Self Scale
This measure assessed the extent to which the participant held a general negative view of himself, and items were derived from the Negative Thoughts About the Self subscale of the Posttraumatic Cognitions Inventory (Foa et al., 1999; five items such as “I am worthless”; α = .91).
The Self-Blame Scale
This measure assessed the degree to which participants continued to reproach themselves for their violent actions (four items such as “I am constantly troubled by my conscience for the crime I committed”; α = .90). The items were derived from the 18-item Guilt Attribution subscale of the Revised Gudjonsson Blame Attribution Inventory (Gudjonsson & Singh, 1989), a scale developed to assess remorse in offenders that has good reliability and transcultural validity (Gudjonsson & Petursson, 1991).
Interpretation of Posttraumatic Symptoms Inventory and Permanent Change scales (Dunmore et al., 1999, 2001)
These scales assessed the extent to which participants interpreted symptoms arising from the assault in a negative way (11 items such as “My reactions since the event show I must be losing my mind”; α = .90) and the extent to which participants perceived that the assault had irreversibly affected them as a person in a negative way (nine items such as “I have permanently changed for the worse”; α = .89). Both measures have been shown to have good reliability and predictive validity in assault survivors (Dunmore et al., 1999, 2001).
The Peritraumatic Dissociative Experiences Questionnaire-rater version (Marmar, Weiss, & Meltzer, 1997)
This 10-item structured interview assesses the degree of dissociation experienced during and immediately after a traumatic event. Each dissociative experience (e.g., derealization, out-of-body experiences) reported by the participant was rated by the interviewer on a 3-point scale ranging from 1 (no) to 3 (threshold). The scale has been shown to have good internal consistency and satisfactory convergent and discriminative validity (Marmar et al., 1997). Internal consistency in the present sample was α = .84.
The Lack of Self-Referent Processing and Data-Driven Processing scales (Halligan et al., 2003)
These eight-item scales assess (a) the extent to which participants failed to process the assault as happening to themselves and to incorporate the experience with other autobiographical information relating to the self (lack of self-referent processing; e.g., “I felt as if it was happening to someone else”; “I felt cut off from my past”) and (b) the extent to which participants primarily engaged in the processing of sensory as opposed to meaning information during the assault (data-driven processing, e.g., “It was just like a stream of unconnected impressions following each other”). Both scales have been shown to have good internal consistency and to predict memory disorganization and the development of PTSD symptoms in trauma survivors (e.g., Halligan et al., 2003). Internal consistencies in the present sample were α = .83 and α = .84, respectively.
Assault narrative task
Participants were asked to give a detailed narrative of the assault by recalling it as vividly, clearly, and in as much detail as possible, while describing events in the order in which they occurred without interruption. All narratives were tape-recorded and transcribed verbatim. Scoring for disorganization followed Foa et al. (1995), in the adaptation by Halligan et al. (2003). Narratives were divided into “chunks” or clauses containing “only one thought, action, or speech utterance.” Three indices of memory disorganization were assessed: (a) repetitions: clauses consisting of repetitions; (b) disorganized thoughts: clear expressions of uncertainty with regard to memory, confusion, or nonconsecutive chunks (e.g., “I know something didn't… at least… they were broken”); and (c) organized thoughts: clauses indicating understanding of what was happening, as a reverse indicator of disorganization. Each score was z transformed in order to control for the variable narrative length, and the composite memory disorganization score was calculated as z(1) + z(2) – z(3) (Halligan et al., 2003). In addition, the rater gave a global disorganization rating, ranging from 1 (not at all disorganized; temporally sequential with high amounts of detail) to 10 (extremely disorganized), after reading each narrative. Interrater reliability (two raters, 20 narratives) showed high agreement for the composite memory disorganization score (r = .92, p < .001) and for the global memory disorganization rating (r = .96, p < .001).
To compare sections of the narrative that corresponded to the main intrusion with other parts of the narrative, global disorganization ratings were done separately for (a) a five-chunk section of the narrative corresponding to the time of the stated intrusion, (b) a randomly selected five-segment section beginning at least 10 chunks after the intrusion in the assault narrative, and (c) a randomly selected five-chunk narrative segment global memory disorganization finishing at least 10 chunks prior to the intrusion. Examination of the assault narratives showed that 11 participants (22.9%) in the intrusion group did not describe their intrusive memory within the narrative. To ensure conservative testing of the hypothesis, these cases were excluded even if the intrusion was from the time period covered in the narrative.
Procedure
The Prison Health Research Ethics Committee (PHREC) approved the study, and the investigators obtained prior written approval of the governors and the lead clinician of the two participating YOIs. The heads of security and operations at the YOIs approved the use of recording equipment. Participant responses were kept confidential, including from the institutional authorities. Participants were not reimbursed.
After the participant had given written informed consent, the semistructured interviews assessing demographic and offense characteristics were administered. Participants then gave a narrative account of the event and filled in the questionnaires. The Intrusion Interview and the PSS-I followed. The session took between 1.5 and 2 hr. All interviews were conducted individually by Ceri Evans. Participants also completed short interviews on rumination and amnesia, which will be presented elsewhere. Where relevant, participants were provided with enlarged rating scales for each questionnaire or interview to consider while the researcher read questions or statements out loud to minimize any potential confounding effect of reading ability.
Statistical Analyses
Data were analyzed with the SPSS for Windows, Version 11.5. Chi-square tests (categorical data, or Fisher's exact test if the chi-square was invalid) or t tests (continuous data, or, when indicated by Levene's equality of variance test, t tests based on unequal variances) were used to compare demographic and assault characteristics of participants with and without intrusions. The cognitive and emotional factors under investigation were analyzed using a hierarchical approach. First, participants with and without intrusions were compared on groups of variables by using multivariate analyses of variance (MANOVAs). If the multivariate test was significant, then univariate comparisons followed. Logistic regression analysis was used to examine whether the cognitive and emotional factors explain the presence of intrusions over and above what can be predicted from demographic factors. Stepwise discriminant function analysis was used to cross-validate the best predictors from the logistic regression with another method. In addition, correlations of the predictors with PTSD symptom severity, as measured by the PSS-I, are reported. The following variables were log transformed to normalize distributions: PSS-I scores, helplessness, self-referent processing, permanent change, interpretation of symptoms, and global narrative disorganization rating. No outliers had to be removed (alpha level was set at p < .05), and all tests are two-tailed.
Results Prevalence of Intrusions
Forty-eight participants (45.7%) reported current intrusive memories of their violent offense. Two additional participants reported having had intrusions in the first few months after the assault that had ceased by the time of the interview (these were included in the no-intrusion group). Six participants (5.7%) met diagnostic criteria for PTSD.
Table 1 shows that the intrusion and no-intrusion groups were comparable for nearly all demographic and assault characteristics, including a history of previous trauma. Participants with intrusions were more likely than those without intrusions to report a history of psychiatric disorders (48% vs. 23%) and a history of previous violent offenses (58% vs. 33%). As to be expected, they also scored higher on the PSS-I.
Demographic and Assault Characteristics
Comparison of Participants With and Without Intrusive Memories
Table 2 compares the intrusion and no-intrusion groups on the cognitive and emotional variables under investigation. The table also shows the correlation of the variables with PTSD symptom severity, as measured by the PSS-I.
Cognitive Variables and Emotions Differences Between Perpetrators With and Without Intrusions and Correlations With Posttraumatic Stress Symptom Severity
Participants with intrusions reported lower antisocial beliefs for the time before the assault. For the measures of perceived threat (perceived physical threat, social image damage), the multivariate analysis of variance (MANOVA) failed to show a significant group difference. The MANOVA of negative emotions showed a significant group difference (p = .049). The intrusion group reported greater intensity of negative emotions during the trauma than the no-intrusion group. The univariate comparisons showed that this was because of greater helplessness and fear in the intrusion group. The intrusion groups did not differ in the extent to which they felt angry or ashamed during the assault, although greater shame correlated with PTSD symptom severity.
The MANOVAs for cognitive processing and memory disorganization also showed significant group differences (ps = .001). Participants with intrusions reported greater dissociation, lack of self-referent processing, and data-driven processing during the assault than those without intrusions, and showed greater disorganization of the assault narrative as indexed by both the composite score and the global rating.
The MANOVA of appraisals of the assault and its aftermath also showed a highly significant group difference (p < .001). The intrusion group scored higher on negative view of self, negative interpretation of symptoms, permanent change, and self-blame than the no-intrusion group.
Further Analyses of the Cognitive Processing and Memory Measures
In the intrusion group, the mean global memory disorganization rating scores for the five-chunk section of the narrative corresponding to the time of the stated intrusion (M = 3.44, SD = 1.14) was significantly greater than a randomly selected five-segment section beginning at least 10 chunks after the intrusion (M = 0.24, SD = 0.58), t(40) = 17.54, p < .001. However, there was no significant difference between the five-chunk narrative segment global memory disorganization ratings at the time of the intrusion and a randomly elected segment finishing at least 10 chunks prior to the intrusion, t(43) = 0.620, p = .54.
Dissociation, data-driven processing and lack of self-referent processing were moderately correlated (rs between .50 and .56, all ps < .001). The two measures of memory disorganization correlated with r = .28 (p = .004).
Regression Analyses
We used a hierarchical logistic regression analysis to test whether the emotional and cognitive factors explained the presence of intrusions over and above what can be explained by demographic factors. Groups of variables were entered in blocks of theoretically linked concepts (Ehlers & Clark, 2000) in approximate temporal order (i.e., antisocial beliefs were entered in Block 2, followed by emotions during the assault in Block 3, cognitive processing and trauma memory measures in Block 4, and appraisals of the event and its aftermath in Block 5). Only variables that had shown significant group differences were entered in the equation. To reduce the risk of multicollinearity, for cognitive processing and memory disorganization only, one measure was entered, and perceived permanent change was dropped from the appraisal block. We expected that each block would add significantly to the explanation of intrusive memories.
Table 3 shows the means and intercorrelations between the predictors. Table 4 shows that, as expected, all blocks of variables significantly increased the amount of variance explained. Demographic variables (past psychiatric history and previous criminal offense) explained 18% of the variance of the presence of intrusive memories of the offense. In Block 2, antisocial beliefs prior to the offense significantly added to the prediction and explained a further 5.4% of the variance (24% explained in total). In Block 3, emotions at the time of the offense (helplessness and fear) explained an additional 10% (34% in total). In Block 4, the measures of cognitive processing and memory disorganization predicted an additional 10% of the variance over and above that explained by the previous measures (44% in total). In Block 5, appraisals of the assault and its aftermath measures contributed a further 16% of the predicted variance (60% in total), and 85% of the participants were correctly identified. In the final model, a history of psychiatric disorders and self-blame explained unique variance at p < .05, and there were trends for dissociation and the composite memory disorganization score at p < .10.
Means, Standard Deviations, and Intercorrelations of Predictors of Intrusive Memories (N = 105)
Logistic Regression Analysis Predicting the Presence and Absence of Intrusive Memories
Discriminant function analysis was used to replicate the result with a different regression method, using variables that discriminated most strongly between the groups. In this analysis, the variables self-blame, history of psychiatric disorders, dissociation, and composite memory disorganization score were selected. These variables had a canonical correlation with intrusive memories of r = .66 (Wilks's λ = .564), χ2(4, 103) = 56.77, p < .001. The standardized discriminant function coefficients for the selected variables were .78, .46, .33, and .32, respectively.
DiscussionIn line with preliminary reports (Kruppa et al., 1995; Spitzer et al., 2001), a substantial proportion (46%) of violent offenders reported intrusive memories of the crimes they committed, and a minority (6%) met diagnostic criteria for PTSD. Given that participants had intentionally harmed other people, it is not surprising that the PTSD rate in this sample was much lower than the rates observed in victims of assault (Andrews et al., 2000; Halligan et al., 2003). Nevertheless, the results indicated that for some perpetrators, their violent crime turns into a traumatic experience. Their distressing intrusive memories resembled those observed in assault survivors (Ehlers et al., 2004).
If the conditions that lead perpetrators to involuntarily reexperience parts of the crimes they committed are better understood, then this will provide an important stepping stone in explaining how PTSD develops in this population. The present study was designed to address this question. Drawing on theoretical models of PTSD and previous research with assault survivors, we chose a range of potential emotional and cognitive predictors of intrusive memories. With the exception of perceived threat, the results supported the hypothesis that the theoretical models and findings on intrusive memories in assault victims generalize to perpetrators. In line with previous research (e.g., Foa et al., 1999; Halligan et al., 2003; Ozer et al., 2003) and theoretical models of PTSD (Brewin et al., 1996; Ehlers & Clark, 2000; Foa & Riggs, 1993; Janoff-Bulman, 1992; Resick & Schnicke, 1993), low antisocial beliefs, negative emotions and problematic information processing during the assault, disorganized trauma memories, and negative appraisals of the trauma and its aftermath, were related to intrusive memories and the severity of PTSD symptoms. The logistic regression analysis further showed that the cognitive and emotional factors under investigation improved the prediction of intrusive memories considerably over and above what can be explained by demographic factors. A history of psychiatric disorders and previous violent offenses explained 18% of the variance. Emotional and cognitive predictors predicted a further 42% of the variance.
Cognitive Schemas Before the Trauma
The data supported the hypothesis that antisocial beliefs would be protective against the development of intrusive memories. This finding is in line with “discrepancy theories” of trauma reactions, in which it is argued that intrusive memories arise from an incompatibility between deeply held beliefs and actual behavior (see Brewin & Holmes, 2003, for a review). Individuals with antisocial beliefs may be less likely to perceive a discrepancy with their values when they behave violently and, hence, less likely to develop intrusive memories. It would be interesting to include a measure of psychopathy in future studies to explore these findings further.
Perceived Threat and Negative Emotions During Trauma
There may be a number of reasons why perceived threat during the assault was not significantly related to intrusive memories. First, whereas perceived threat to life is predictive in victims of assault, it may be less relevant for perpetrators who inflict harm. Second, we may not have assessed other important aspects of threat that are important for perpetrators. One interesting dimension for future studies may be perceived moral breach during the assault. A qualitative analysis (Evans et al., in press) included the suggestion that in some cases, a sense of having acted unacceptably or in a way that the community would not condone seemed to be linked to the development of intrusive memories. In support of this argument, self-blame after the assault showed a strong association with intrusions in the present study.
Our finding that participants with intrusive memories reported to have felt greater helplessness and fear during the assault than those without intrusions corresponds well to Criterion A2 of the DSM–IV diagnostic criteria for PTSD (American Psychiatric Association, 1994). It is interesting that the emphasis on helplessness and fear replicated in the present sample of perpetrators, as one may have assumed that other emotions may be more relevant in this population. The helplessness factor may, however, have somewhat different connotations in perpetrators than in victims of violence in that this scale may have reflected feelings of degradation rather than helplessness in defending oneself. The findings that anger and shame were not significantly related to intrusions is in line with Brewin et al.'s (1996) hypothesis that emotions such as shame are secondary emotions that only develop after the trauma.
Cognitive Processing and Memory Disorganization
As in previous research (Ozer et al., 2003), dissociation during the trauma was associated with intrusive memories and PTSD symptoms. In line with Ehlers and Clark's (2000) model, data-driven processing and lack of self-referent processing were also related to reexperiencing symptoms and correlated moderately with dissociation. As in Halligan et al.'s (2003) study, memory disorganization was related to intrusive memories and PTSD symptoms. The two measures of memory disorganization only showed a small correlation with each other. This is consistent with reviews suggesting that different measures assess different components of problematic trauma memory retrieval (Ehlers et al., 2004; McNally, 2003). For example, gaps in memory increase the global disorganization rating but not the composite memory disorganization score. Furthermore, not all parts of the trauma memory may show deficits, especially if the trauma is a prolonged event. In the present study, we found some preliminary support for Ehlers et al.'s (2004) suggestion that the deficits in intentional recall should be most marked for those parts of the trauma that are reexperienced. The section of the assault narrative corresponding to the intrusive memory was rated as more disorganized than a subsequent section of assault memory transcript. However, no significant difference was found when comparing the intrusion segment with a narrative segment before the intrusion. This negative finding may have been influenced by the fact that we excluded 23% of the intrusion group who did not mention the part corresponding to the intrusion in their narratives. This procedure may have been overly conservative, as one may argue that omissions in the narrative may indicate difficulties with intentional retrieval or even a gap in memory.
Appraisals of the Trauma and Its Aftermath
In support of theories that emphasize the role of negative appraisals of the trauma and its aftermath in PTSD (Ehlers & Clark, 2000; Foa & Riggs, 1993; Resick & Schnicke, 1993), we found that such appraisals related to intrusive memories and PTSD symptom severity in perpetrators of violent crime. The appraisal factors explained an additional 16% of the variance over and above the other variables included in the logistic regression analysis. The findings parallel those obtained in victims of assault and torture (Dunmore et al., 1999, 2001; Ehlers et al., 2000; Foa et al., 1999; Halligan et al., 2003).
Limitations
The present study had several limitations. First, the study was cross-sectional, and the results remain correlational. It is therefore not possible to establish causal relationships between the cognitive and emotional factors under investigation and intrusive memories. Second, participants were interviewed after being convicted for the crime, which meant that cognitive processing and emotions were assessed many months after the event. It is therefore possible that recall was imprecise and may have been affected by subsequent events such as interrogations and court proceedings. It is unlikely, though, that these events would have created a systematic bias in favor of the hypotheses under investigation. Most likely, they may have contributed to the error variance. Moreover, time since the assault was not related to intrusions. It is possible, however, that experiencing intrusive memories may have led the participants to reevaluate the perceived threat during the assault. Third, the findings rely on self-report, and we cannot rule out that participants did not always give valid answers. However, there was no incentive to distort the answers because participants had already been convicted, the results of the interviews were confidential and did not have any influence on their sentence and conditions in prison, and there was no financial incentive. Furthermore, the main dependent variable—presence of intrusive memories—was not based on simple participant endorsement but on detailed descriptions, which were rated by experts on the phenomenology of intrusive memories in patients with PTSD. Similarly, interviewer ratings were used to measure dissociation. Fourth, our assessment of memory disorganization rests upon the assumption that disorganization in a narrative reflects disorganization in an underlying memory representation. However, disorganization in the narrative may result from other processes, such as problems with expressing the contents of memory or censoring. Fifth, we used 16 cognitive processes and emotions as predictor variables in a study with 105 participants. Even though we used a hierarchical approach to data analysis, the possibility of chance findings cannot be ruled out. However, all positive findings, with the exception of the role of antisocial beliefs, replicate findings of other studies with assault victims, which supports the validity of these findings. Sixth, some of the items of the Permanent Change scale may have been affected by the experience of being in prison and may have somewhat different meanings for perpetrators and victims. Seventh, the present findings were obtained with a group of young, predominantly male perpetrators of violent crime. Masculine confrontations, which are essentially “honor” contests in public settings and involve alcohol, were overrepresented in the present sample, whereas sexual or domestic homicides were less frequent than might be expected in studies involving older prisoners (Daly & Wilson, 1988; Polk, 1994). It is unclear whether such differences would affect the generalizability of these findings to other offender populations. Finally, the study focused on intrusive memories rather than on PTSD, and it remains to be tested whether the factors highlighted in the present article also predict PTSD in this population. The correlations of the predictor variables with the PSS-I suggest that this is likely to be the case. Future studies will need to investigate what factors determine whether perpetrators who have intrusive memories of their crimes develop the full syndrome of PTSD.
Conclusion
In summary, the results support the hypothesis that similar mechanisms explain intrusive memories in victims and perpetrators of violence. They may also have clinical implications for the treatment of violent offenders, as there are effective cognitive–behavioral treatment programs for distressing, intrusive traumatic memories and PTSD (e.g., Ehlers, Clark, Hackmann, McManus, & Fennell, 2005; Foa & Rothbaum, 1998; Resick & Schnicke, 1993). However, the issue of whether distressing intrusive memories of the offense in perpetrators should be treated is not straightforward. From a clinical perspective, it can be argued that individuals deserve treatment for their mental distress, regardless of their perceived responsibility for their distress. Furthermore, it could be argued that, without treatment, the offender's risk of future violent behavior may be increased because of general symptoms, such as increased irritability, or by specific triggering of intrusive memories and flashbacks. A counterargument would be that intrusive memories, and the distress associated with these memories, provide regular, uncomfortable reminders of the crime and help to reduce the risk of violent reoffending. Whether treatment of intrusive memories in violent offenders has an impact on subsequent offenses will need to be tested empirically.
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Submitted: November 28, 2005 Revised: July 18, 2006 Accepted: July 20, 2006
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Source: Journal of Consulting and Clinical Psychology. Vol. 75. (1), Feb, 2007 pp. 134-144)
Accession Number: 2007-00916-014
Digital Object Identifier: 10.1037/0022-006X.75.1.134
Record: 26- Title:
- Just showing up is not enough: Homework adherence and outcome in cognitive–behavioral therapy for cocaine dependence.
- Authors:
- Decker, Suzanne E.. New England Mental Illness Research Education and Clinical Center, West Haven, CT, US, suzanne.decker@yale.edu
Kiluk, Brian D.. Department of Psychiatry, Yale University School of Medicine, CT, US
Frankforter, Tami. Department of Psychiatry, Yale University School of Medicine, CT, US
Babuscio, Theresa. Department of Psychiatry, Yale University School of Medicine, CT, US
Nich, Charla. Department of Psychiatry, Yale University School of Medicine, CT, US
Carroll, Kathleen M.. Department of Psychiatry, Yale University School of Medicine, CT, US - Address:
- Decker, Suzanne E., VA Connecticut Health Care System, 950 Campbell Avenue (151D), West Haven, CT, US, 06516, suzanne.decker@yale.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 84(10), Oct, 2016. pp. 907-912.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 6
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- cocaine, homework, psychotherapy, cognitive–behavioral therapy, treatment outcome
- Abstract (English):
- Objective: Homework in cognitive–behavioral therapy (CBT) provides opportunities to practice skills. In prior studies, homework adherence was associated with improved outcome across a variety of disorders. Few studies have examined whether the relationship between homework adherence and outcome is maintained after treatment end or is independent of treatment attendance. Method: This study combined data from 4 randomized clinical trials of CBT for cocaine dependence to examine relationships among homework adherence, participant variables, and cocaine use outcomes during treatment and at follow-up. The data set included only participants who attended at least 2 CBT sessions to allow for assignment and return of homework (N = 158). Results: Participants returned slightly less than half (41.1%) of assigned homework. Longitudinal random effects regression suggested a greater reduction in cocaine use during treatment and through 12-month follow-up for participants who completed half or more of assigned homework (3-way interaction), F(2, 910.69) = 4.28, p = .01. In multiple linear regression, the percentage of homework adherence was associated with greater number of cocaine-negative urine toxicology screens during treatment, even when accounting for baseline cocaine use frequency and treatment attendance; at 3 months follow-up, multiple logistic regression indicated homework adherence was associated with cocaine-negative urine toxicology screen, controlling for baseline cocaine use and treatment attendance. Conclusions: These results extend findings from prior studies regarding the importance of homework adherence by demonstrating associations among homework and cocaine use outcomes during treatment and up to 12 months after, independent of treatment attendance and baseline cocaine use severity. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Impact Statement:
- What is the public health significance of this article?—This examination of data from 4 randomized trials suggests that homework adherence in cognitive–behavioral therapy for cocaine dependence is associated with better cocaine outcomes during treatment and through 12 months follow-up, independent of the effects of treatment attendance or baseline cocaine severity. This study joins others in demonstrating an association between homework adherence and symptom change during CBT, and suggests homework assignment and adherence warrant continued study as key ingredients in CBT. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Cognitive Behavior Therapy; *Drug Dependency; *Homework; *Treatment Compliance; *Treatment Outcomes; Cocaine
- PsycINFO Classification:
- Cognitive Therapy (3311)
- Population:
- Human
Male
Female - Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Timeline Followback
Substance Use Calendar - Grant Sponsorship:
- Sponsor: VA Connecticut Health Care System, US
Recipients: Decker, Suzanne E.
Sponsor: VISN 1 MIRECC
Recipients: Decker, Suzanne E.
Sponsor: National Institute on Drug Abuse, US
Grant Number: R01DA015969-09S1 and P50DA09241
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study; Treatment Outcome
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Jul 25, 2016; Accepted: May 10, 2016; Revised: Feb 1, 2016; First Submitted: May 29, 2015
- Release Date:
- 20160725
- Correction Date:
- 20160926
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/ccp0000126
- PMID:
- 27454780
- Accession Number:
- 2016-36128-001
- Persistent link to this record (Permalink):
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Just Showing Up Is Not Enough: Homework Adherence and Outcome in Cognitive–Behavioral Therapy for Cocaine Dependence / BRIEF REPORT
By: Suzanne E. Decker
New England Mental Illness Research Education and Clinical Center, West Haven, Connecticut, and Department of Psychiatry, Yale University School of Medicine;
Brian D. Kiluk
Department of Psychiatry, Yale University School of Medicine
Tami Frankforter
Department of Psychiatry, Yale University School of Medicine
Theresa Babuscio
Department of Psychiatry, Yale University School of Medicine
Charla Nich
Department of Psychiatry, Yale University School of Medicine
Kathleen M. Carroll
Department of Psychiatry, Yale University School of Medicine
Acknowledgement: Suzanne E. Decker is supported by VA Connecticut Health Care System and VISN 1 MIRECC. Other authors’ work for this study was supported by National Institute on Drug Abuse (NIDA) grants: R01DA015969-09S1 and P50DA09241. Kathleen M. Carroll is a Member in Trust of CBT4CBT LLC. All authors were involved in the planning, interpretation, and writing of this article. Kathleen M. Carroll was the lead investigator on studies from which these data are drawn. Suzanne E. Decker developed the initial plan for data analysis and wrote the first manuscript draft; Charla Nich, Suzanne E. Decker, and Theresa Babuscio conducted statistical analyses; Theresa Babuscio and Tami Frankforter conducted data management; Charla Nich provided statistical and interpretive consultation; Brian D. Kiluk and Kathleen M. Carroll provided editorial assistance. The NIDA, Department of Veterans Affairs, Veterans Affairs Connecticut Healthcare System, and MIRECC had no further role in study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the manuscript for publication. Views expressed in this article are those of the authors.
A central component of cognitive–behavioral therapy (CBT) is emphasis on between-session practice assignments, or “homework.” Homework provides opportunities to practice new skills, test new ideas, and generalize learning outside of session (Kazantzis, Whittington, & Dattilio, 2010). Meta-analyses suggest homework adherence (partial or full completion of homework) has been associated with improved CBT outcome across a variety of disorders (Kazantzis et al., 2010; Mausbach, Moore, Roesch, Cardenas, & Patterson, 2010). Other dimensions of homework include its quality (Detweiler & Whisman, 1999). In meta-analyses comparing outcomes of treatments with and without homework, a small-to-medium mean effect size (d = 0.48) favored treatments with homework (Kazantzis et al., 2010). Homework adherence and improved symptoms have been found to be associated in several studies (e.g., Bryant, Simons, & Thase, 1999; Burns & Spangler, 2000; although see also Weck, Richtberg, Esch, Höfling, & Stangier, 2013). In the addictions literature, homework adherence has been associated with improved symptoms in three studies (meta-analysis, r = .27; Mausbach et al., 2010) and associated with reduced cocaine use, as indicated by both self-report and cocaine-negative urine toxicology screens (Carroll, Nich, & Ball, 2005; Carroll et al., 2008).
The relationship between homework adherence and symptom change may take several forms, including a direct impact of homework on symptoms or reflecting a third variable, such as client motivation (Burns & Spangler, 2000; Gonzalez, Schmitz, & DeLaune, 2006). Homework adherence has been associated with participant variables indicating clinical severity (e.g., previous depressive episodes; Bryant et al., 1999), although direct correlations between initial symptom severity and homework adherence have not been consistently found (e.g., Bryant et al., 1999; Weck et al., 2013). Other potential correlates of homework adherence include therapist, working alliance, task characteristics (Detweiler & Whisman, 1999), and therapist competence (Bryant et al., 1999; Kazantzis, Ronan, & Deane, 2001; Weck et al., 2013). Although treatment attendance has been associated with homework adherence (Burns & Spangler, 2000), it has not been examined in all studies (e.g., Weck et al., 2013). Client ratings of homework’s helpfulness have been correlated with treatment attendance in cocaine dependence treatment (Siqueland et al., 2004), suggesting that attendance and client opinions on homework are related but distinct. As prior studies have not consistently included attendance, less is known about whether homework adherence is associated with symptom change when accounting for treatment attendance.
The present trial extends earlier findings that showed an association between homework adherence and cocaine outcomes (Carroll et al., 2005) by using data pooled across four independent outpatient CBT trials, resulting in a larger and more diverse sample, and including data through 12 months after treatment’s end. To avoid overlap (Carroll et al., 2005), analyses were conducted with and without data from this study.
MethodData for these analyses were drawn from four randomized clinical trials (A: Carroll et al., 1998; B: Carroll et al., 2016; C: Carroll et al., 2004; D: Carroll et al., 2008) evaluating CBT for cocaine dependence. Trials were conducted by the same research group, using similar assessment batteries and procedures. Participants were included in this analysis if they were assigned to receive CBT and attended at least two CBT sessions, thus having the opportunity to be assigned and to return homework at least once. Three studies (A, B, C) used a 12-week manualized CBT protocol, with follow-up assessments at 1, 3, 6, and 12 months posttreatment. Therapist training included a didactic seminar and completion of at least one closely supervised training case; all sessions were recorded for fidelity monitoring. The remaining study (D) evaluated an 8-week computerized CBT protocol, with follow-up assessments at 1, 3, and 6 months posttreatment. As Study A, B, C’s fidelity measures were similar but not identical, and Study D represented a different modality of treatment, therapist fidelity data were not included in these analyses. All studies were reviewed and approved by the institutional review board; participants provided written informed consent.
Measures
Cocaine use
We evaluated cocaine use with self-report (percent days abstinent) and a biological measure (percentage of urine specimens that were negative for cocaine metabolites). Self-reported cocaine use in the 28 days prior to randomization, during the treatment period, and at each follow-up period was assessed using the Substance Use Calendar (Carroll et al., 2004), a calendar-format interview based on the Timeline Followback (Sobell & Sobell, 1992). Urine samples were collected weekly or more during treatment and at each follow-up interview, tested for cocaine metabolites, and compared with standard cutoff values (benzoylecgonine level <300 ng/mL considered cocaine-negative). The percentage of cocaine-negative urine samples during treatment was calculated by dividing the number of cocaine-negative urine samples by the number of urine samples obtained. Further detail on original trials and operationalization of outcome variables is available in Carroll et al. (2014).
Homework assignment and adherence
Homework was assigned at most sessions starting with Session 1 (Studies A, B, C) or at the completion of each computerized CBT module (Study D). A dichotomous report of homework assignment at each session was generated from therapist report (Studies A, C) or computer (Study D); in Study B, the report of homework assignment was generated at the subsequent session (i.e., was homework completed, not completed, or not assigned?). Study therapists recorded whether participants had partially or fully completed the previous week’s homework assignment at each weekly session in Studies A, B, and C. In Study D, the computer program asked each participant if they had completed homework at the start of each session. Homework adherence was calculated by dividing the number of homework assignments reported as partially or fully completed by number of homework assignments given. For participants who terminated treatment early, the calculation was based on the data from available sessions (number of homework assignments reported as partially or fully completed divided by the number of sessions in which homework was checked) to avoid artificial deflation because of missing homework adherence data for the last session.
Analyses
Descriptive analyses (mean, percent) were used to examine homework adherence; t tests, ANOVA, and Pearson’s correlation were used to evaluate relationships among homework adherence, participant variables, and treatment attendance. We hypothesized that greater homework adherence would be associated with lower levels of self-reported cocaine use and more cocaine-negative urine toxicology screens. Random effects regression models were used to evaluate relationships of homework adherence to self-reported cocaine use outcomes across time (from baseline to treatment end or to 12-month follow-up). For the random regression models, homework adherence was categorized as an ordinal variable with three levels: (1) no homework adherence; (2) some homework adherence, but no more than 50%; or (3) more than 50% of homework assignments completed. For models using data from baseline to treatment end, time was log transformed to account for the high rate of change in the first weeks of treatment. Piecewise models (Singer & Willet, 2003) were used to evaluate cocaine use from baseline to 12-month follow-up, with both treatment month and treatment phase (Weeks 1–12 vs. follow-up) as independent variables. These analyses were replicated in a subsample excluding the previously examined Study C (n = 110). To separate homework adherence from treatment attendance, analyses were replicated in treatment completers only (n = 81); a third model included treatment completion as an independent variable. For cocaine-negative urine toxicology screens, longitudinal models were precluded by having only one urine result at follow-up points. The relationship between homework adherence and the percentage of cocaine-negative urine toxicology screens from baseline to treatment end was examined using multiple linear regression; multiple logistic regression was used for urine toxicology screen result at each follow-up point. Models included baseline frequency of cocaine use (self-reported cocaine use in 28 days prior to study), percentage of sessions attended, and study protocol. As the sample size did not permit multiple regression without Study C, we examined partial correlations among homework and percentage of cocaine-negative urine toxicology screens for each study, controlling for baseline cocaine use and attendance.
Results Sample Characteristics
Across the four studies, 243 participants were assigned to CBT. Of these, 158 (65.0%) who attended at least two CBT sessions were included in this report. Participant demographic information across the four studies is presented in Table 1. The sample was largely male (n = 115, 72.8%), and African American (n = 68, 43.0%) or Caucasian (n = 73, 46.2%). Although there were no significant gender or educational differences across studies, participants in Trial D were more likely to be employed, married, referred by the criminal justice system, or on public assistance (see Table 1). Participants reported they used cocaine a mean of 13.8 of the 28 days prior to randomization (SD = 8.51). Across studies, participants attended more than 50% of CBT sessions offered; post hoc testing indicated higher levels of attendance in Study C than Study B (mean difference = 19.0, SD = 5.8, p = .01, 95% confidence interval [CI] [4.4, 35.5]). There were no significant main effects of study on self-reported cocaine abstinence (see Table 1), suggesting outcomes were similar across CBT protocols and combining data was appropriate.
Descriptive Statistics for Sample
Homework Adherence
The mean number of homework assignments given and reported as partially or fully completed were 5.7 (SD = 3.3) and 2.6 (SD = 2.6), respectively, such that participants returned 41.1% (SD = 32.5; range = 0% to 100%) of assigned homework. Percentage of homework assignments completed did not differ by gender, race, education, referral by the criminal justice system, previous outpatient mental health treatment, lifetime diagnoses of depression, alcohol use disorder, or anxiety disorder, or current antisocial personality diagnosis (results available on request). Percentage of homework assignments completed was not significantly correlated with percentage of sessions attended (r = .14, p = .08), nor with baseline cocaine use frequency (r = .03, p = .71).
Homework Adherence and Self-Reported Cocaine Use Over Time
Random effects regression indicated a significant reduction in frequency of cocaine use across time. In the model using data from baseline to treatment end (Table 2, Model 1), an interaction between percent of homework adherence and time indicated greater cocaine use reduction in those who completed more than 50% of homework assignments compared with those with 50% or less homework adherence, or those who completed no homework (Homework × Time, F[2, 390.24] = 6.77, p = .00). A second model included data from baseline through 12-month follow-up (Table 2, Model 2). A three-way interaction between homework group, time, and phase indicated that although the change in cocaine use was greatest during active treatment for those with homework adherence more than 50% of the time compared with those with 50% or less homework adherence, the rate of change in cocaine use during follow-up was less than that during treatment, F(2, 910.69) = 4.28, p = .01, but the effect of homework group remained significant through follow-up. When these models were repeated in the subsamples excluding Study C (n = 110), or in treatment completers only (n = 81), power was limited. However, the patterns of results did not change direction. To examine whether the relationship of homework to cocaine use was because of treatment attendance, an additional model included a dichotomous indicator of treatment attendance (completed treatment vs. dropped out). The three-way interaction between homework, time, and phase remained statistically significant, indicating support for the finding that completion of greater than 50% of homework assigned was associated with less cocaine use during treatment and through follow-up, and suggesting that the relationship of homework to reduced cocaine use was independent of treatment attendance.
Longitudinal Models on Self-Reported Cocaine Use Over Time
Homework Adherence and Cocaine-Negative Urine Toxicology Screens
The multiple linear regression model indicated that greater homework adherence was associated with more cocaine-negative urine toxicology screens during treatment, even with treatment attendance in the model (Table 3; β = 0.17, t = 2.59, p = .01, sr2 = 0.17). Partial Pearson’s correlations on homework and percentage of cocaine-negative urine toxicology screens during treatment, controlling for baseline cocaine frequency and attendance, had small samples, and only that of Study C reached statistical significance. At 1, 3, and 6 months follow-up, logistic regression models for cocaine-negative urine toxicology screen result were significant (see Table 4). Homework adherence was associated with cocaine-negative urine toxicology screen at 3-month follow-up (β = 1.02, 95% CI [1.00, 1.04], p = .01). The model was not significant at 12-month follow-up. Small sample size did not permit meaningful comparison of cocaine-negative urine toxicology screen results for each study at each time point.
Multiple Linear Regression Analysis and Partial Correlations on Percentage of Cocaine-Negative Urine Toxicology Screens
Logistic Regression on Cocaine-Negative Urine Toxicology Screen at Follow-Up
DiscussionThis examination of pooled data from four randomized controlled trials evaluating clinician- and computer-delivered CBT indicated that homework adherence was associated with significantly less cocaine use from baseline to treatment end on two indicators (self-report and cocaine-negative urine toxicology screen). Longitudinal models suggested that participants with greater than 50% homework adherence had a greater reduction in cocaine use than those with less homework adherence during treatment and up to 12 months after, even when accounting for treatment attendance. Greater homework adherence was associated with cocaine-negative urine toxicology screens during treatment and at 3 months follow-up.
Correlations and bivariate analyses indicated homework adherence was not associated with baseline cocaine use or other participant variables. This was consistent with other studies showing no direct correlation between homework adherence and initial symptom severity (Bryant et al., 1999; Burns & Spangler, 2000) or other participant variables (Weck et al., 2013).
Why might homework adherence be associated with improved outcomes? Although homework may be related to participant motivation (Detweiler & Whisman, 1999; Gonzalez et al., 2006), its association with outcomes during and after treatment was independent of treatment attendance. Homework adherence may be associated with acquisition of new skills (Kazantzis et al., 2010), or increases in coping skill quality and quantity (Carroll et al., 2005); skill quality has been shown to mediate the relationship between CBT and substance use treatment outcomes (Kiluk, Nich, Babuscio, & Carroll, 2010). The persistence of homework’s association with reduced cocaine use at up to 12 months after treatment also suggests homework may have been associated with learning generalization, although these analyses could not evaluate relationships between homework adherence and skills acquisition or generalization across these four studies.
Despite the emphasis on homework in most CBT protocols and manuals, data on the association of homework and outcomes are still relatively sparse. To date, this is the first report evaluating the role of homework in substance use disorder treatment using combined samples from multiple studies and using longitudinal models to evaluate cocaine use through 12-month follow-up. Other strengths include drawing data from well-controlled randomized controlled trials based on the same CBT manual; evaluation of cocaine use via both self-report and biological samples; and use of weekly reports of homework adherence rather than retrospective reports (Bryant et al., 1999). Limitations of the current study include the limited range of indicators potentially associated with homework across trials, such as acquisition of coping skills, motivation, or therapist competence, as these were not collected uniformly across all studies. Other limitations include the absence of data on homework quality or a continuous measure of homework adherence; missing data, particularly at follow-up points; and varying data collection methods on homework completion across studies (Mausbach et al., 2010). These analyses were conducted without correction for multiple analyses. Nevertheless, this report adds to the accumulating evidence that homework is associated with improved outcome in CBT, that its positive effects remain even after treatment ends, and that it may be a factor associated with the durability of CBT in many samples.
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Carroll, K. M., Kiluk, B. D., Nich, C., DeVito, E. E., Decker, S., LaPaglia, D., . . .Ball, S. A. (2014). Toward empirical identification of a clinically meaningful indicator of treatment outcome: Features of candidate indicators and evaluation of sensitivity to treatment effects and relationship to one year follow up cocaine use outcomes. Drug and Alcohol Dependence, 137, 3–19. 10.1016/j.drugalc.dep.2014.01.012
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Submitted: May 29, 2015 Revised: February 1, 2016 Accepted: May 10, 2016
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Source: Journal of Consulting and Clinical Psychology. Vol. 84. (10), Oct, 2016 pp. 907-912)
Accession Number: 2016-36128-001
Digital Object Identifier: 10.1037/ccp0000126
Record: 27- Title:
- Linear and nonlinear growth models: Describing a Bayesian perspective.
- Authors:
- Depaoli, Sarah. Psychological Sciences, School of Social Sciences, Humanities, and Arts, University of California, Merced, CA, US, sdepaoli@ucmerced.edu
Boyajian, Jonathan. Psychological Sciences, School of Social Sciences, Humanities, and Arts, University of California, Merced, CA, US - Address:
- Depaoli, Sarah, Psychological Sciences, School of Social Sciences, Humanities, and Arts, University of California, 5200 N. Lake Road, Merced, CA, US, 95343, sdepaoli@ucmerced.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 82(5), Oct, 2014. Special Issue: Advances in Data Analytic Methods. pp. 784-802.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 19
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - ISBN:
- 1-4338-1955-4
- Language:
- English
- Keywords:
- Bayesian estimation, growth curve modeling, growth mixture modeling, nonlinear growth models, Markov chain Monte Carlo
- Abstract:
- Objective: Conventional estimation of longitudinal growth models can produce inaccurate parameter estimates under certain research scenarios (e.g., smaller sample sizes and nonlinear growth patterns) and thus lead to potentially misleading interpretations of results (i.e., interpreting growth patterns that do not reflect the population patterns). The current article used patterns of change in cigarette and alcohol abuse prevalence and depression levels to demonstrate an alternative method for estimating growth models more accurately under these conditions, namely, via the Bayesian estimation framework. This article acts as an introduction and tutorial for implementing Bayesian methods when examining growth or change over time, particularly nonlinear growth. Method: The National Longitudinal Survey of Youth 1997 database was used to highlight different linear and nonlinear (quadratic and logistic) growth models via growth curve modeling (GCM) and growth mixture modeling (GMM). The specific focus was on changes in cigarette/alcohol consumption and depression throughout adolescence and young adulthood. Specifically, a nationally representative group of individuals between the ages of 12 and 16 years were assessed at 4 time-points for levels of cigarette consumption, alcohol use, and depression. Results: The results for each example illustrated different patterns of linear and nonlinear growth via GCM and GMM through the versatile Bayesian estimation framework. Conclusions: Growth models may benefit from the Bayesian perspective by incorporating prior information or knowledge into the model, especially when sample sizes are small or growth is nonlinear. A step-by-step tutorial for assessing various growth models via the Bayesian perspective is provided as online supplemental material. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Statistical Estimation; *Statistics; Models
- Medical Subject Headings (MeSH):
- Adolescent; Bayes Theorem; Female; Humans; Linear Models; Longitudinal Studies; Male; Models, Statistical
- PsycINFO Classification:
- Statistics & Mathematics (2240)
- Population:
- Human
- Location:
- US
- Age Group:
- Childhood (birth-12 yrs)
School Age (6-12 yrs)
Adolescence (13-17 yrs) - Supplemental Data:
- Other Internet
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Dec 23, 2013; Accepted: Oct 7, 2013; Revised: Sep 26, 2013; First Submitted: Oct 31, 2012
- Release Date:
- 20131223
- Correction Date:
- 20140922
- Copyright:
- American Psychological Association. 2013
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0035147; http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0035147.supp(Supplemental)
- PMID:
- 24364797
- Accession Number:
- 2013-44747-001
- Number of Citations in Source:
- 62
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- Database:
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Linear and Nonlinear Growth Models: Describing a Bayesian Perspective
By: Sarah Depaoli
Psychological Sciences, School of Social Sciences, Humanities, and Arts, University of California, Merced;
Jonathan Boyajian
Psychological Sciences, School of Social Sciences, Humanities, and Arts, University of California, Merced
Acknowledgement: The authors would like to thank Rose Scott and Eric Walle for helpful advice and comments on an earlier version of this article.
Many processes within psychological research fluctuate or change over time. In the context of clinical research specifically, treatment response to cognitive behavioral therapy may fluctuate throughout the duration of treatment. Verbal abilities may change in children with autistic spectrum disorder in relation to therapy sessions. In addition, depression, stress levels, and substance abuse patterns are all likely to change throughout young adulthood. In order to properly examine these dynamic processes along a continuum of time, techniques based on growth modeling can be employed.
Latent growth curve modeling (GCM) is one modeling technique commonly found in the literature that captures complex developmental patterns of change over time. Growth modeling has increased in prevalence in the applied psychological literature over the last decade. Although growth models are typically used to capture linear growth, there are arguably many processes found within clinical research that do not represent linear patterns of change. It may be that a patient initially responds quickly to a treatment and then the improvement levels off over time. Likewise, a patient may show initial response (or growth) to a treatment and then revert back to the initial status after more time passes. In these cases, a linear model would not capture the true essence of change underlying the data patterns; for examples of nonlinear growth see Heimberg and Becker (2002); Nishith, Resick, and Griffin (2002); and Roifman, Levison, and Gelfand (1987). As a result, nonlinear growth models may be necessary to explore when assessing complex change or growth patterns over time. Some specific hypothetical examples of common (non)linear growth patterns found in psychological research are presented in a subsequent section.
This (non)linear growth modeling technique has also been extended to incorporate multiple latent (or unobserved) groups of individuals, where each latent group is characterized by its own pattern of growth or change over time. This extension that allows for modeling different growth patterns across latent groups is known as growth mixture modeling (GMM). Bauer (2007) presented citation counts showing that GMM has been used at an increasing rate in the applied literature. Specifically, three of the seminal articles introducing GMM to applied researchers are B. O. Muthén and Muthén (2000); B. Muthén and Shedden (1999); and Nagin (1999). Each of these articles was cited in the applied literature 109, 105, and 200 times, respectively, over the course of 7 years.
Despite the growing popularity in usage, these (non)linear growth models are not without problems. Specifically, some methodological research using Monte Carlo (simulation) methods (see e.g., Depaoli, 2012; Depaoli, 2013; Hipp & Bauer, 2006; S.-Y. Kim & Mun, 2012) have shown that the conventional estimation methods used with growth modeling (e.g., maximum likelihood via the expectation maximization algorithm) can produce inaccurate parameter estimates under certain conditions commonly found in the applied literature (e.g., smaller sample sizes and nonlinear growth patterns). Inaccurate parameter estimates can therefore potentially yield misleading and inappropriate interpretations of results (i.e., interpreting growth patterns that do not reflect the population patterns).
Goals of the Current Paper and Intended AudienceThe aim of the current article is to introduce an alternative method for estimating growth models that has been shown to produce more stable and accurate results in simulation studies (see e.g., Depaoli, 2013, in press; S.-Y. Kim & Mun, 2012). This method employs an estimation technique referred to as the Bayesian estimation framework. A recent article by Bolt, Piper, Theobald, and Baker (2012) in the Journal of Consulting and Clinical Psychology introduced this Bayesian framework into the clinical psychology literature as a method for obtaining path analysis results for smoking cessation.
The Bayesian estimation framework has been shown to improve the accuracy of parameter estimates for more complex models, such as those modeling nonlinear growth and latent classes (i.e., mixtures). Thus, the current article will extend the Bayesian process described in Bolt et al. (2012) to growth modeling and present a tutorial for using this method aimed at clinical researchers who are unfamiliar with these estimation techniques. An overview of linear and nonlinear growth modeling is also provided for readers that are less familiar with these growth modeling techniques.
One goal of the current article is to focus on the flexibility of Bayesian estimation framework and to highlight some of the nuances that are present when implementing Bayesian estimation (e.g., tracking [non]convergence). Another goal here is to highlight the superiority of the Bayesian framework in simulation where nonlinear growth is captured more accurately compared to conventional estimation methods. Specifically, the Bayesian estimation framework has been found to outperform conventional estimation methods in the context of linear and nonlinear growth; some of the findings in the simulation literature are highlighted here. Note that the intention of the current article is not to claim that this is the first Bayesian article to address growth modeling. Rather, this article is meant to make Bayesian estimation techniques more accessible in order to advance the way in which clinical psychologists assess growth or change over time. Overall, our hope is that this article acts as a user-friendly introduction to Bayesian methods that will aid in promoting the use of these flexible and (oftentimes) superior techniques in clinical research.
The Bayesian estimation framework will be implemented in the context of several examples capturing different patterns of change and growth in cigarette smoking prevalence, alcohol use, and depression levels throughout adolescence and young adulthood. However, the reader should be aware that the modeling and estimation techniques demonstrated here can be applied to a wide range of clinical contexts that exhibit growth and change over time.
Organization of the Current ArticleThis article is organized as follows. First, a description of the kind of theoretical problems that can be addressed via growth models (both GCM and GMM) will be presented along with details about these growth modeling techniques. Next is a brief discussion of some of the Monte Carlo (simulation) results that have shown that growth models perform poorly under certain conditions when conventional estimation methods are used. The subsequent section presents details surrounding the conventional estimation method and the Bayesian estimation framework that can be used as an alternative method for more accurately estimating growth and change over time. Several key features of the Bayesian framework are detailed here, and hypothetical examples are provided as discussion points for these features of the estimation process.
Next, several clinically relevant examples using the National Longitudinal Survey of Youth (NLSY) 1997 database (Ohio State University, 1997) are presented to illustrate the estimation of different forms of growth within GCM and GMM via the Bayesian estimation framework. Each example presented is coupled with a detailed explanation of: the estimation process, the data used in the example, and annotated software code and instructions for implementing the relevant software program(s). This article concludes with a discussion of the risks and benefits for implementing the Bayesian estimation framework when examining different forms of growth and change in longitudinal data.
Modeling Linear and Nonlinear GrowthIn general, growth modeling represents a modeling technique that includes observed outcomes (continuous or categorical) and continuous latent (or unobserved) variables. The observed outcomes represent the repeated measures data collected over time. Likewise, the latent variables represent the growth parameters (or growth factors), which, in the case of linear GCM, are the initial status at Time 0 (the intercept) and the growth rate between time intervals (the slope). These growth parameters are considered to be random effects in the model since a mean and a variance are estimated for each of them.
To illustrate GCM, consider the scenario depicted in Figure 1 where stress levels (STRESS) are assessed at the end of each year of college as an attempt to examine change in stress throughout college. In this linear GCM example, there are four waves of assessment and therefore four observed outcomes (denoted by squares). The latent variables (denoted as circles) represent the initial stress status during the freshman year of college (Intercept) and the growth rate of stress levels across the four time points (Slope). This model is a restricted structural equation model in that the paths linking STRESS and the intercept are fixed at 1.0 and the paths linking STRESS and the slope are fixed as the time metric across time. In particular, for a linear growth model with four equally spaced time-points, the paths are fixed, respectively, at 0, 1, 2, and 3, which represent linear time. Likewise, for a quadratic model the paths are fixed, respectively, at 0, 1, 4, and 9, which represent quadratic time. These restrictions are what make this form of a structural equation model represent GCM. Assume, for example, that the average initial status was equal to 25 points on a stress-level assessment and the average growth rate was equal to 5 points per year. This would indicate that the college students received an average of 25 points on the initial assessment of stress during their freshman year. On average these scores would increase by 5 points between assessments. Specifically, if a student’s score was 25 points in their freshman year; their score would rise on average to 30 points in their sophomore year. In this case, we can picture the example simply as a line containing an intercept and a linear slope. This relationship depicted in the line is often referred to as the average growth trajectory within growth modeling.
Figure 1. An illustration of growth curve modeling assessing change in stress levels throughout college.
A separate issue from (non)linearity is the number of growth trajectories modeled in a single analysis. An extension of GCM, referred to here at GMM, is a method used to account for differences in growth patterns in groups of individuals (or latent classes) that are not directly observed but are inferred through the model based on observed data patterns. In order to contrast GCM and GMM, equations for both models are presented here. For GCM, the measurement part of the model is represented by the following:
where yi is a vector of repeated measures outcomes for person i, Λy represents a matrix of factor loadings with T (number of time-points) rows and k (number of latent factors) columns (T × k matrix). The first column is fixed at all 1s to correspond with the intercept and the remaining k − 1 columns represent constant time values (e.g., 0, 1, 2, 3 for linear and 0, 1, 4, 9 for quadratic). The ηi term is a vector of latent growth parameters (e.g., intercept and slope) that has k elements. Finally, εi represents a vector of normally distributed residuals. The growth parameters can still be considered as random variables in this modeling perspective, and this is addressed in the structural part of the model. In this case,
where ηi still represents a vector of the growth parameters α is a vector of factor means, and ζi is a vector of normally distributed deviations of the parameters from their respective population means. For GMM, there is one slight difference in the measurement and structural model equations compared to GCM. In particular, we see an added subscript “c,” which denoted latent class membership. Specifically we see the following:
Equations 3 and 4 (representing GMM) include latent class subscripts that signify that the vector of growth parameters (ηic) and the factor means (αc) are now allowed to vary across latent classes. In practical terms, this means that each cth latent class is now allowed to have a different intercept and slope compared to the other latent classes.
B. Muthén and Shedden (1999) and Nagin (1999) were the first to publish articles explicitly adding to the complexity of the GCM by allowing for multiple latent classes within the model, thus creating variants of GMM. These latent classes within GMM are assumed to represent different underlying populations in the data, and each of these populations in theory has their own respective growth patterns. If multiple latent classes truly exist (meaning that data were collected from multiple underlying populations), and these classes are collapsed together and assumed to represent a single population, then potentially valuable information about different growth patterns in the data will be lost. Oftentimes latent classes can be justified by identifying classes with substantively different growth patterns. Likewise, substantive differences between classes may be determined based on how the latent classes differ substantively on some covariate (B. O. Muthén, 2004). For more information on selecting the optimal number of latent classes, see Nylund, Asparouhov, and Muthén (2007).
For other nonlinear growth patterns, the written form of the GCM looks quite a bit different from Equations 1 and 2. Specific to this article, a logistic GCM can be written as
where yit represents the repeated measured variable y for person i at time-point t, β0i is a lower asymptote for person i when growth is positive, β0i + β1i is the upper asymptote for person i when growth is positive (vice versa when growth is negative), Ait represents the logistic function that is expanded in Equation 6, λi is the inflection point or the time where the rate of change is at its maximum for person i, and αi represents the slope at the inflection point for person i (Grimm & Ram, 2009). To allow this model to differ across latent classes, the following parameters would all have “c” subscripts in Equations 5 and 6 to denote latent class membership: β0ic, β1ic, λic, and αic; also note that Figure 9 (described below) details how these model parameters translate to growth trajectories.
Figure 9. Trajectory plots for (a) empirical smoking linear examples, (b) empirical alcohol use quadratic examples, (c) hypothetical logistic example, (d) empirical depression level logistic examples. GCM = growth curve modeling; GMM = growth mixture modeling; C1/C2 = Class 1/Class 2.
Examples of Previous Clinical Research Implementing GCM/GMM
In general, growth modeling is a technique that has been quite prevalent within clinical research. In fact, there has been a growing body of research published in the Journal of Consulting and Clinical Psychology that has implemented either GCM or GMM in some capacity. For example, Sikkema et al. (2012) used GCM to examine changes in traumatic stress and avoidant coping for individuals living with HIV/AIDS and child sexual abuse. Kofler et al. (2011) presented GCM results to capture whether depressive symptoms predict delinquent behavior during late-adolescence and teen years. DiLillo et al. (2009) showed via GCM that newlywed couples reporting marital difficulties tended to continue to have these difficulties over the next 2 years. Finally, Spoth, Trudeau, Guyll, Shin, and Redmond (2009) used GCM to examine the rate of increase in substance use based on additional outcomes such as drunkenness and other alcohol-related issues.
Within the context of GMM, Henderson, Dakof, Greenbaum, and Liddle (2010) uncovered two distinct classes of children with different patterns of substance use severity (high and low severity) in the context of multidimensional family therapy. Likewise, Stulz, Thase, Klein, Manber, and Crits-Christoph (2010) used GMM to define latent classes each exhibiting different patterns of change of depression severity during different treatment phases for chronic depression (i.e., cognitive behavioral analysis system of psychotherapy, pharmacotherapy using Nefazodone, and a combination of both). Finally, Greenbaum, Del Boca, Darkes, Wang, and Goldman (2005) identified five different classes with respect to alcohol consumption patterns within a freshman college student population: light-stable, light-stable plus high holiday, medium-increasing, high- decreasing, and heavy-stable.
Hypothetical Examples Illustrating (Non)linear Patterns in GCM/GMM
Within GCM and GMM, there are a variety of linear and nonlinear growth patterns that are possible to estimate depending on the empirical question at hand. The current article addresses three of the most common dynamic processes found in psychological research, representing linear, quadratic, and logistic growth. A hypothetical example of each of these growth patterns is presented next for GCM and GMM. Each of the following plots represents an example where data were collected across four waves to examine changes in depression levels.
The top row of Figure 2 illustrates data for a single group of individuals that represent linear growth patterns in depression levels across time. The left plot in this figure shows individual growth trajectories and the right plot shows the estimated linear growth trajectory that captures the overall growth pattern for this group of individuals. Notice how the individual trajectories all seem to represent linear growth across the time-points. The estimated growth curve trajectory indicates that the average initial depression level was at about 60 (with higher scores representing higher depression), with an average growth rate (or slope) of about one unit between time points–indicating a relatively flat growth rate. Figure 2 also illustrates data that represent quadratic change in depression across time. Specifically, the growth trajectory in the right plot (middle row) illustrates the relatively smooth negative (downward) curvature in growth over time. Finally, the bottom row of Figure 2 illustrates a logistic growth rate in depression across time. In particular, the growth trajectory in the right plot illustrates the sigmoidal growth pattern in that there is a relatively level depression growth rate, a sharp increase in depression, and then a leveling off toward the final wave of data collection.
Figure 2. Hypothetical examples illustrating growth curve modeling with linear, quadratic, and logistic growth. The vertical y-axis represents initial status of the outcome measure (e.g., initial depression levels), and the horizontal x-axis represents the time-points when data on the outcome measure was collected (e.g., four equally spaced time-points).
This hypothetical example looking at changes in depression levels across time can also be viewed in the context of GMM, where more than one latent class exists in the data. For the purposes of illustration, these examples for GMM include two latent classes, each with different growth trajectory patterns. The top row of Figure 3 illustrates two latent classes with different linear growth patterns. The left plot shows individual growth trajectories, and it is clear from the individual data patterns that there are two separate classes represented in these data. It should be noted that actual, substantive data would likely not look as “clean” as these data with respect to visual class separation. The right plot depicts these two classes by using two separate estimated growth trajectories to summarize the corresponding groups of individuals. In this example, there are two different classes representing different rates/patterns of change in depression. The first class has a much higher initial depression level at the first time-point compared to the second class. In addition, the second class has a slightly steeper growth rate compared to the first class. Likewise, the middle row of Figure 3 shows two classes with differing growth rates, but these classes each depict quadratic (or curved) growth across time. Specifically, the first class has a higher initial depression level compared to the second class. However, the second class shows a relatively sharp increase in depression between time-points 2 and 3, whereas the first class shows a more stable decrease in depression. Finally, Figure 3 illustrates a logistic growth rate in depression across time for two separate classes. In particular, one class illustrates a much sharper increase in depression levels, whereas the other class shows a more leveled increase over the time points.
Figure 3. Hypothetical examples illustrating growth mixture modeling with linear, quadratic, and logistic growth. The vertical y-axis represents initial status of the outcome measure (e.g., initial depression levels), and the horizontal x-axis represents the time-points when data on the outcome measure was collected (e.g., four equally spaced time-points). The lines in the plots on the right represent the growth patterns for the two different latent classes.
Estimation Issues for (Non)linear Growth Models
Conventional estimation of growth models typically implements maximum likelihood via the expectation-maximization (EM) algorithm (Dempster, Laird, & Rubin, 1977). However, this conventional estimation approach can produce substantively inaccurate estimates under many conditions commonly found within psychological research. Specifically, simulation research has indicated that maximum likelihood-based approaches do not properly recover model parameters (i.e., the estimates obtained are inaccurate) under cases of lower sample sizes. Boomsma (1987) and Jackson (2001) found that sample sizes at or below 50 for a single-class (i.e., nonmixture) model do not produce accurate parameter estimates for a small (cross-sectional) structural equation model. Obtaining inaccurate parameter estimates as a result of smaller samples is a problematic issue with respect to clinical research given that larger sample sizes may not be available in some clinical settings.
In the context of growth, Depaoli (2013) found that the conventional estimation approach was unable to properly estimate growth trajectories when sample sizes were at 150 cases and three latent classes were being estimated (i.e., each latent class had 50 cases, which is comparable to the Boomsma, 1987, and Jackson, 2001, findings discussed above); in addition, many model parameters were also poorly estimated even with a larger total sample size of 800 cases. In this same study, it was also shown that nonlinear growth models (i.e., models showing quadratic growth over time) were estimated with very little accuracy. Although the conventional estimation method produced some inaccuracies when growth was specified as being linear over time, these inaccuracies were more severe when growth was specified as being quadratic over time across all conditions presented in that article. None of the conditions of quadratic growth were able to properly recover the growth trajectories that were defined in the simulations (even with larger sample sizes); additional details of this simulation study are presented in the next section.
As discussed, simulation research has shown that latent variable models (specifically growth models) are not accurately estimated under conditions of smaller sample sizes, or when growth over time is nonlinear. Adequate estimation is more consistently obtained within simulations under conditions of very high sample sizes (e.g., n = 3,000) and when growth is linear. However, these conditions may not be particularly realistic in empirical research settings. Large sample sizes are not always practical to obtain. Likewise, many dynamic processes do not grow or change in a linear fashion; nonlinearity is a common feature of psychological phenomena assessed over time. These issues have led researchers to consider alternative estimation procedures in search of an estimator that can be reliable under conditions more typical of real data (i.e., smaller samples and nonlinearity in growth). The following section introduces one of the options for an alternative estimator, namely, the Bayesian estimation framework.
An Introduction to the Bayesian Estimation Framework
The goal of this section is to provide an accessible introduction to Bayesian methods in the context of modeling change over time. For additional introductory resources about Bayesian estimation, see Bolstad (2007) or Hoff (2009). For a more advanced treatment of Bayesian methods, see Carlin and Louis (2009) or Gelman, Carlin, Stern, and Rubin (2004).
Within the conventional estimation approach that employs classical frequentist methods, population parameters are assumed to be fixed and unknown quantities. For example, within GCM, the intercept and slope parameters would be assumed to be fixed quantities in the population that are then estimated based on sample data. The specific goal within this approach is typically to estimate a parameter (e.g., the slope) and then form a confidence interval surrounding this parameter estimate to see if it is significantly different from zero (or some other point value specified in the null hypothesis). In the case of a growth model, the estimate for the slope would be assessed against a point null hypothesis to determine whether the growth rate was significantly different from zero (i.e., if there was significant change over time).
Within the Bayesian framework, model parameters are weighted by priors that reflect levels of (un)certainty about parameter values. The end goal within the Bayesian paradigm is to estimate (technically, to converge upon) the probability distribution for a given parameter called the posterior distribution. The posterior distribution is a product of the data and prior beliefs or knowledge about the distribution of the parameter being estimated.
Specifically, prior beliefs or knowledge about the possible shape of this distribution for the parameter are directly incorporated into the model estimation process. These prior beliefs are weighted by the sample data in order to form an approximation of the posterior distribution for each of the model parameters (Rupp, Dey, & Zumbo, 2004). Priors can be useful for incorporating information about model parameters in addition to that provided by the sample data. This integration of prior beliefs into the estimation process has been found in simulation research to improve the accuracy of parameter estimates obtained for various forms of growth models (Depaoli, 2013).
Specifically, Figure 4 illustrates a small portion of the simulation results presented in Depaoli (2013). This figure contains plots showing absolute percent bias results for GMM parameter estimates obtained under conventional maximum-likelihood estimation and results from the Bayesian framework using informative priors. Bias results greater than 10% are typically deemed to represent a problematic level of bias that can alter interpretation of substantive results. Figure 4 illustrates that maximum-likelihood estimation had consistently high levels of bias in these study conditions, and this pattern remained for all other conditions not shown here. In contrast, the Bayesian approach was able to produce more accurate results in this simulation. Note that several different types of priors were examined in Depaoli (2013) and the interested reader is referred to the full article for more detail on the performance of priors. Overall, the Bayesian framework appears to be a viable and more accurate alternative when estimating linear and nonlinear growth models, especially when sample sizes are small.
Figure 4. Partial simulation results from Depaoli (2013) showing absolute bias levels for parameter estimates under convention and Bayesian estimation levels. Note that latent class proportions of 70/20/10 indicate that 70% of the people were in Class 1, 20% were in Class 2, and 10% were in Class 3.
The process for computing the posterior distribution for a model parameter is an iterative process that involves implementing Markov chain Monte Carlo (MCMC) techniques where a Markov chain is constructed for each model parameter using Monte Carlo (simulation) techniques. This chain represents an approximation of the posterior distribution which is then summarized and used to produce model estimates. Unlike conventional estimation algorithms (e.g., EM), MCMC relies on sampling techniques to estimate the model parameters and form the Markov chain.
The specific aim of MCMC is to reproduce the posterior density. Solving for the posterior requires high-dimension integration, and this makes it difficult to compute directly. As a result, solving for the posterior is often carried out through sampling repeatedly from the distribution (J.-S. Kim & Bolt, 2007). Specifically, a sampling process is implemented that samples observations from the posterior distribution in order to create a sampled approximation of the posterior distribution. These samples are then combined to form the Markov chain representing an approximation of the population posterior distribution, which captures specific distributional information about the population parameter. By drawing enough samples from the posterior, characteristics such as the mean, mode, and variance can be uncovered with respect to the posterior density.
There are various methods that can be used to sample from the posterior density. The first method was introduced by Metropolis and colleagues (Metropolis, Rosenbluth, Rosenbluth, & Teller, 1953) and has served as a basis for all other sampling methods developed within MCMC. The Metropolis sampling algorithm as we use it today is actually a generalization introduced by Hastings (1970). The Metropolis-Hastings algorithm incorporated some relaxed assumptions that produced a more flexible algorithm. One special case of the Metropolis-Hastings algorithm that is probably one of the more common sampling methods is called the Gibbs sampler (Casella & George, 1992; Geman & Geman, 1984). The Gibbs sampling technique samples each parameter individually with respect to its conditional distribution and treats all other parameters in the model as known. Specifically, one parameter is updated (or sampled) with respect to the conditional distribution given the remaining variables under the posterior distribution. This updating process typically occurs in a particular fixed order for the parameters and is sometimes referred to as scanning (Geyer, 1991). A full description of sampling methods is beyond the scope of this article, however, the interested reader can be referred to J.-S. Kim and Bolt (2007); Casella and George (1992); and Chib and Greenberg (1995).
As samples are being drawn from the posterior distribution via the MCMC estimation process, convergence of the chain is monitored. When convergence is detected, then it can be concluded that an adequate number of samples were drawn from the posterior distribution; specific details for assessing chain convergence are presented below. Once convergence has been established, then it is possible to determine the characteristics of the posterior distribution such as the mean and the variance (J.-S. Kim & Bolt, 2007). These characteristics of the posterior distribution are then used to summarize features of the population parameter. For example, the mean of the posterior is often used as the estimate for the population parameter (e.g., the mean of the posterior distribution for a slope parameter can be interpreted as the estimate for the slope).
One potential benefit of using the Bayesian estimation framework via MCMC is that convergence rates are typically improved over conventional estimation methods—especially in the context of latent class models (e.g., GMM). Hipp and Bauer (2006) showed that, as the number of GMM latent classes in a simulation study increased, the convergence rates dramatically decreased. Likewise, Depaoli (2013) showed that, as the latent classes became less distinct from one another (i.e., poor class separation resulting in similar growth trajectories across classes), the convergence rates dropped substantially under a conventional maximum-likelihood based estimator. However, the Bayesian estimation framework has been shown in simulation to produce vastly improved convergence rates (Depaoli, 2013; S.-Y. Kim & Mun, 2012).
The sections presented next all highlight different key features of the Bayesian estimation framework. Specifically, different types of prior distributions that reflect the prior knowledge and beliefs of the researcher are discussed; then methods for defining priors in an empirical research scenario are presented. This is followed by a discussion of assessing convergence and an issue referred to as label switching that must be addressed when estimating latent class models.
Prior Distributions
When using Bayesian methods to estimate a growth model, something called a prior distribution is specified for each of the model parameters (e.g., the intercept, linear slope, quadratic slope, logistic parameters, etc.). A prior distribution (or prior) incorporates knowledge (or uncertainty) about the corresponding model parameter into the estimation process. As mentioned above, the Bayesian estimation framework combines the actual data and the prior knowledge/information about the model parameters together in order to derive the final model results. These priors often take the form of a known probability distribution (e.g., the normal distribution), and these probability distributions incorporate a certain amount of information into the estimation process that reflects the amount of information the researcher chooses to incorporate into the model. For example, if a researcher examining patterns of change in depression over time in a clinical population had reason to believe (e.g., through previously published research, personal professional experience, or consultation with other experts in the field) that depression severity increases at a certain rate in this population over time, then the prior distribution corresponding with the slope (rate of change) would reflect this knowledge.
The level of (un)certainty in a prior is manipulated through the specific features of the prior distribution, and these features are called hyperparameters in the Bayesian context. For example, a normal distribution is defined through a mean and a variance term; if the mean and variance are known, then we have enough information to construct the normal distribution. The mean and variance terms for the normal distribution would be considered the mean and variance hyperparameters for a normal prior. The amount of knowledge incorporated into a prior is directly controlled through these hyperparameters.
For example, the left column in Figure 5 shows three normal distributions that all have different variance hyperparameters. Figure 5a shows a normal distribution that has a variance of 1, Figure 5b has a variance of 10, and Figure 5c has a variance of 100. We can see that Figure 5a is illustrating much more certainty about the possible values of the parameter because the spread of this distribution covers a much smaller range of possible values compared to the plots in Figures 5b and 5c. In contrast, Figure 5c shows very little certainty about the possible values of the parameter because there is a much larger spread of possible values that fall under this prior. In general, the variance hyperparameter dictates the possible range of values for the corresponding parameter being estimated (e.g., the slope).
Figure 5. Prior distributions illustrating different levels of informativeness.
Akin to the variance hyperparameter of the normal prior, the mean hyperparameter can also be manipulated. Figures 5d, 5e, and 5f all represent different mean hyperparameter values for each of the three variance hyperparameter values shown in the left column of the figure.
Specifically, Figure 5d shows that the priors are centered at 5, Figure 5e shows priors that are centered at 30, and Figure 5f shows priors that are centered at 50. The mean hyperparameter specifies the value that will receive the highest probability (or weight) for that parameter value in the estimation process.
If the researcher was very certain about the rate of increase in depression, then a prior representing this high degree of certainty would be called an informative prior. Informative priors typically contain strict numerical information that is sometimes crucial to the estimation of the model. Specifically, the hyperparameters for an informative prior are fixed to express specific information about the model parameters being estimated. This information can come from a variety of places, including an earlier data analysis or the published literature. Figure 5a is an example of an informative prior in that this distribution is indicating that the possible values for the depression growth rate are hovering very closely to 10. Specifically, it would be very rare to obtain a growth rate of 15 under this prior distribution.
In complete contrast to an informative prior would be a prior distribution that shows very little (if any) certainty for the value of a parameter. A prior that represents very little certainty (or knowledge) about a parameter value is referred to as a noninformative prior or as a diffuse prior. Using the example where change in depression is assessed over time, a noninformative prior would represent the scenario where the researcher had little to no information about the change rate of depression in this clinical population. The noninformative prior captures this high degree of uncertainty by placing equal (or near equal) probability on a very large range of possible values for that parameter. For example, Figure 5c represents a noninformative normal prior in that near-equal probability (or weight) is given to a very large range of possible values for the growth rate of depression. In this example, the entire spectrum of slope values shown in the figure has near-equal probability.
Although a full spectrum of informativeness exists between an informative prior and a noninformative prior, it is worth noting that a distinction in the Bayesian literature has been made for a middle-range prior that represents a moderate amount of prior knowledge. This middle-range prior is referred to as a weakly informative prior, and it represents less information than the informative prior, but it incorporates much more information than the noninformative prior. An example of a weak prior can be found in Figure 5b, which illustrates a prior that has a bit more uncertainty than the informative prior because Figure 5b is showing more spread (variance) in the distribution. On the other hand, Figure 5b has incorporated much more information about the parameter value than Figure 5c, which gives almost equal probability to a large range of possible values. In some sense, weakly informative priors can be considered to be more useful than diffuse priors because, although they are still relatively noninformative, these priors do provide some indication about the range of plausible values for a parameter without being as restrictive as an informative prior.
Figure 6 provides an example of the potential impact that prior distributions can have on the posterior. The three plots in this figure represent partial results from a simulation study (see Depaoli, in press) that examined the impact of priors that looked quite different from the likelihood (i.e., the data distribution). In these plots, the likelihood looks exactly the same. The manipulated feature across plots is the distribution of the prior. In particular, Plot A has a very informative prior with little variance, Plot B has a slight less-informative prior (e.g., a weak prior), and Plot C has the relatively weakest prior out of all of them with the largest variance in the distribution. In these plots, it can be seen that the prior with the least amount of variance (i.e., Plot A) has the most influence over the posterior distribution and the prior with the most variance (i.e., Plot C) has the least influence.
Figure 6. Three examples of the impact of prior distributions and likelihoods (data) on posterior distributions. Plot A illustrates the most informative prior of the three plots, and Plot C illustrates the least informative prior.
Each of the three levels of informativeness discussed here is implemented regularly in the Bayesian literature. Further, it is possible to incorporate any combination of these levels of informativeness into the same model. For example, a researcher assessing change in depression may have strong prior beliefs about the initial depression level of the clinical population and would therefore specify an informative prior for the intercept of the growth model. However, the researcher might be fairly uncertain about the growth or change rate of depression and, as a consequence of this uncertainty, would specify a weak or noninformative prior for the slope.
Defining priors
There are many different methods that researchers can use for defining priors for model parameters. One such method for defining priors would be to draw from global information from a large body of past research examining similar phenomena. This method is akin to meta-analysis in that information from a larger body of research (e.g., on depression growth rates) would be incorporated into the model priors. Another method would be to define priors based on a single previous data analysis in a (very) similar topic area. For example, Gelman, Bois, and Jiang (1996) presented a study looking at physiological pharmacokinetic models where prior distributions for the physiological variables were pulled from results from an earlier sample that was collected. Although these prior distributions were considered to be quite specific, they were also considered to be reasonable given that they resulted from a similar analysis computed on another sample of data.
Finally, another common method for defining priors is to elicit them from content experts. For depression change rates over time in a clinical population, the priors corresponding with the initial depression level (intercept) and the rate of change over time (slope) could be determined based on expert knowledge about depression in this population. Expert elicitation is quite common, and in some research scenarios (e.g., when studying a population exhibiting a rare disorder that has not been sampled from before) it is the only viable method for defining priors. For a more detailed discussion of the elicitation of priors, see O’Hagan (1998).
MCMC Convergence Diagnostics
As discussed above, throughout the sampling process, samples are drawn iteratively to produce a series of observations comprising a Markov chain. This chain is then used to help form the posterior distribution that is used to derive parameter estimates and credibility intervals (the Bayesian version of a confidence interval) for a given parameter. The convergence of this chain is essential for making accurate inferences using the Bayesian parameter estimates and credibility intervals. The current section details the different convergence diagnostics commonly implemented in the Bayesian estimation framework. Practical implementation of these diagnostics is discussed and demonstrated in the example sections presented below, along with detailed interpretations of Bayesian parameter estimates and credibility intervals.
Before the samples can be drawn to form the chain, initial parameter values (or starting values) must be specified in order to give the chain a starting state. These initial starting values can be randomly generated or they can be determined by the practitioner (e.g., based on educated guesses for parameter values). In some cases (e.g., with more complex models such as nonlinear GMM) the starting values should be carefully selected as to ensure a suitable starting position of the chain. Also, multiple Markov chains can be used to diversify the starting placement of chains; this topic is discussed in more detail below.
These samples that are drawn to form the chain are not independent of one another. In fact, sampling mechanisms rely on the previous sample state when determining what the next sample state will be. Some sampling algorithms (e.g., Metropolis Hastings; see Chib & Greenberg, 1995; Hastings, 1970; Metropolis, Rosenbluth, Rosenbluth, & Teller, 1953) will even at times use the current sample state as the subsequent sample state, making dependence among samples an issue of which to be mindful. Since neighboring samples are not independent, it is common (and often necessary) to discard the samples comprising the first part of the chain. Discarding this section of the chain is necessary because these beginning samples are more influenced by the initial starting values specified. This beginning portion of the chain that is discarded (called the burn-in phase) is not used in the final posterior distribution that determines parameter estimates, etc. For example, if the full length of the chain contained 2,000 samples total, the first 500 samples may be discarded as the burn-in phase while the remaining 1,500 samples would comprise the posterior distribution. The goal is to discard the beginning portion of the chain that demonstrates sample-instability and high dependence on the starting values, thus ensuring that the last portion of the chain comprising the posterior distribution contains stable samples that are independent from initial starting values.
There are two main methods that can be used to help determine the length of the burn-in phase. The first method is to visually inspect a plot that illustrates all of the samples comprising the chain. Visually, “stability” within a chain should look like a tight, horizontal band akin to that shown in Figure 7a. The fact that the y-axis interval in Figure 7a is quite wide [–100,100] just speaks to the scaling and the variance of this parameter. Figure 7a is showing consistency in the variance of the posterior distribution because the vertical width of the chain remains consistent across all of the chain iterations. The actual interval reflected on the y-axis is of less concern as long as the variance within the chain (i.e., the vertical width) is stable and consistent.
Figure 7. Three convergence plots illustrating evidence of convergence (a), evidence of nonconvergence (b), and nonconvergence becoming stable and converging (c).
Any fluctuation from this tight, horizontal band would indicate instability (and therefore nonconvergence) in the chain. Chain instabilities typically indicate that a longer burn-in phase is needed before the chain stabilizes. However, it should also be noted that there are some cases where convergence is not obtained (e.g., when informative priors are wrong), regardless of the length of the burn-in phase. In the case of Figure 7b, a longer burn-in phase is necessary in order to discard this portion of the chain that is rather unstable. The length of the required burn-in phase is in part a function of the complexity of the model. Less complex models (e.g., linear GCM) may require a shorter burn-in phase (e.g., 100 iterations) because stability is reached with fewer iterations. Note that it is not uncommon to discard tens (or even hundreds) of thousands of iterations comprising the burn-in phase for more complex models. As a last example, Figure 7c shows a convergence plot that illustrates that the beginning portion of the chain is rather unstable and the last portion is rather stable. Through visual inspection of this chain, the researcher may determine that the first 500,000 iterations should be discarded as the burn-in and the remaining iterations should be used as the approximation of the posterior.
Solely using visual inspection of convergence plots is often not deemed as a sufficient assessment of convergence. Viewing a tight, horizontal chain does not indicate that convergence was actually obtained since this method is more likely to be an indicator of nonconvergence (Mengersen, Robery, & Guihenneuc-Jouyax, 1999). Visual inspection of these plots can misguide the researcher if fluctuations in the chain coinciding with nonconvergence are not visually depicted. Given that merely viewing these plots may not be sufficient in determining convergence (or nonconvergence), it is also common to reference additional diagnostics. It is also the case that visual inspection of the chain may be misleading if the section of the chain being inspected is not long enough or does not incorporate an adequate burn-in phase. As a result, other statistical methods can be used to aid in establishing the length of the burn-in and post-burn-in phases.
The second method for determining the length of the burn-in phase is to use a diagnostic called the Raftery-Lewis diagnostic (Raftery & Lewis, 1992). The Raftery-Lewis diagnostic provides some general guidelines about the length of the burn-in phase and how long the post burn-in phase (i.e., the posterior) portion of the chain should be. The Raftery-Lewis diagnostic is specified for a particular quantile of interest that is user-specified (e.g., 0.25, 0.5, or 0.75 quantiles). If, for example, the Raftery-Lewis diagnostic used the 0.5 quantile (reflecting the center of the posterior distribution), then a lower bound value would be produced that would represent the minimum number of post burn-in iterations needed to accurately estimate the 0.5 quantile. Often the Raftery-Lewis diagnostic is used iteratively in that an initial analysis without a burn-in phase may be conducted first. After running the Raftery-Lewis diagnostic, information about the burn-in phase and the length of the post burn-in phase would be obtained. A final model using this information can then be estimated and interpreted.
After the length of the burn-in phase has been determined, the rest of the chain being used to construct the posterior distribution should be evaluated. Even with discarding the burn-in phase that contains unstable or dependent samples, the post burn-in phase should also be assessed to ensure that proper convergence was obtained. Lack of chain convergence can sometimes be visible (see, e.g., Plot b in Figure 7). However, not all convergence problems are so drastically visible and, as a result, there are additional statistical diagnostics that can be used to evaluate chain stability. Given that the very nature of the MCMC process is to converge in distribution rather than to a single estimated value, it can be rather difficult to assess convergence of a chain. As a result, it is common for practitioners to assess several different diagnostics that examine varying aspects of convergence. Three of the more commonly implemented diagnostics will be discussed here; namely, the Geweke convergence diagnostic, the Heidelberger and Welch diagnostic, and the Brooks, Gelman, and Rubin diagnostic.
The Geweke convergence diagnostic (Geweke, 1992) is used to determine whether the first part of a chain differs significantly from the last part of a chain. For example, the first 10% of the chain and the last 50% of the chain would be compared by computing a z-statistic for the sample means corresponding with each of these sections of the chain. A z-statistic falling in the extreme tail of a standard normal distribution suggests that the sample from the beginning of the chain has not yet converged (Smith, 2005). It is important to note that there should be a sufficient number of iterations between the two samples to ensure the means for the two samples are independent.
The Heidelberger and Welch convergence diagnostic (Heidelberger & Welch, 1983) is a stationary test that determines whether the last part of a Markov chain has stabilized. Specifically, if there is evidence of nonstationarity, the first 10% of the iterations will be discarded, and the test will be repeated either until the chain passes the test or more than 50% of the iterations are discarded. If a parameter does not pass this test, then this is an indication that the chain needs to run longer before achieving convergence.
The last convergence diagnostic discussed here is the Brooks, Gelman, and Rubin convergence diagnostic (see e.g., Gelman, 1996; Gelman & Rubin, 1992a, 1992b). This method includes a diagnostic called the potential scale reduction (PSR) factor which is based on the theory of analysis of variance. The PSR factor was initially developed to assess convergence among several parallel chains with varying starting values. During the MCMC process, the researcher can either choose to construct a single chain for each model parameter or multiple chains for each model parameter. In the multiple-chain case, the PSR factor would represent a ratio of the between-chain variation and the within-chain variation. A PSR factor near 1.0 would result in evidence of chain convergence; this indicates that the variation between chains and the variation within chains is comparable, which implies that the different chains were essentially overlapping one another (i.e., they converged together). However, this diagnostic can also be used to assess convergence in a single chain by comparing the first portion of the post-burn-in iterations to the last portion of the chain. In other words, a PSR factor near 1.0 would indicate that the variation in the beginning portion of the post burn-in portion of the chain is comparable to the variation in the last portion of the chain.
The examples presented below implemented each of the convergence diagnostics discussed here. Examples in Mplus used the Brooks, Gelman, and Rubin diagnostic and examples in OpenBUGS (or WinBUGS) used the Raftery-Lewis, Geweke, and Heidelbeger and Welch diagnostics by loading the convergence diagnostic and output analysis (CODA; Best, Cowles, & Vines, 1996) files produced by OpenBUGS (or WinBUGS) into the Bayesian Output Analysis (BOA) program (Smith, 2005) interface for R (R Development Core Team, 2008). Specific instructions, screen shots, and data for implementing all of these examples are available as online supplemental material.
Label Switching
One issue that can arise when estimating mixture models via MCMC sampling is referred to as label switching. It occurs when the ordering of mixture classes arbitrarily changes during the MCMC chain. Not only can this affect the final estimates produced but label switching can also complicate the assessment of convergence. Label switching is especially an issue when multiple chains have been used since the within-chain ordering of classes could differ across chains (Farrar, 2006; Vermunt, 2008). The label switching problem is quite common for mixture models estimated via MCMC, and it is therefore important to understand the causes and the proposed solutions (Jasra, Holmes, & Stephens, 2005).
When label switching occurs, the sampler is not able to distinguish between the mixture classes, and as a result, the class labels will arbitrarily switch between mixtures within the chain (Jasra et al., 2005). Switching mixture interpretations within a chain results in meaningless MCMC output since posterior means are no longer directly interpretable. There are some reparameterization techniques that can be employed in the initial model specification which can help prevent label switching. For example, the mean (or mixture class proportion) for one group can be constrained to be larger than the mean for another group (e.g., InterceptClass1 < InterceptClass2 < InterceptClass3, which is akin to what was implemented in the current examples). Each iteration of the sampler is computed such that the specified constraint is satisfied (Jasra et al., 2005). This method is referred to as an identifiability constraint and can help prevent the chain from arbitrarily sampling an alternative latent class midchain (see e.g., Diebolt & Robert, 1994; Frühwirth-Schnatter, 2001). This form of constraint is artificial since it is not rooted in any genuine knowledge or belief about the model but merely comprised out of convenience.
The purpose of this constraint is to solve the labeling problem. Essentially, this technique is a condition on the parameter space where only one permutation (ordered combination) can satisfy this condition. Satisfying this condition can remove the possibility of label switching (Jasra et al., 2005). In general, this is viewed as common practice for handling label switching, but it may not be the most appropriate method to employ. There are several examples of situations where this method does not remove the issue of label switching (see e.g., Jasra et al., 2005; Stephens, 2000). This is in part due to the fact that there are many choices of identifiability constraints that are ineffective in preventing label switching. If the parameter to place the restraint on is not chosen carefully (e.g., a parameter on which latent classes differ substantially), then posterior-symmetry can remain and label switching can still occur. In the event of identifiability constraints not working, then the researcher must employ some sort of relabeling algorithm that can reorder (and relabel) the ordering of the chain to remove the presence of label switching; for more information about relabeling algorithms (which are not implemented here) see Farrar (2006) and Stephens (2000).
ExamplesThree main examples are presented here, and each of these examples demonstrates different types of linear or nonlinear growth for GCM and GMM. These examples are all based off of data obtained from the NLSY 1997 database (Ohio State University, 1997). There were approximately 9,000 nationally representative teenagers between the ages of 12 and 16 years at the first wave of data collection in this database. A smaller subsample of 500 cases was used for each of the examples presented below. Note that this subsample of 500 cases represents the first 500 cases containing complete data across the first four waves of data collection. The total sample size of 500 was chosen a priori for a two-class solution, which yields approximately 250 individuals per class in this example—assuming equal class sizes. This sample size remains consistent with the approximate class size presented in the simulation findings detailed in Depaoli (2013), which used a total sample size of 800 for a three-class solution and equal class sizes. It should be noted here that these examples were constructed for pedagogical purposes and that there is not necessarily any clinical significance in the substantive findings presented here.
Linear Growth
The first set of examples illustrates the application of a linear growth model in the context of GCM and GMM using the Bayesian framework. First, results for GCM are presented where just a single growth trajectory is obtained and interpreted. Next, results for GMM are presented where two latent classes are modeled, each with their own estimated patterns of growth. For these two examples, the Mplus software program (Version 7; L. K. Muthén & Muthén, 1998–2012) was used to implement Bayesian estimation. In addition, the priors specified for these examples were the default diffuse priors implemented in Mplus. The growth parameters (intercept and slope) received normally distributed priors with a mean hyperparameter of 0 and a variance of 1010 (N[0,1010]). In this case, the priors reflect total uncertainty with respect to growth rates and class sizes in the context of GMM; all priors can be found in the online supplemental material and Table 1.
Prior Distributions for All Examples: Linear, Quadratic, and Logistic
For the linear growth models presented here, the repeated measures outcome captures smoking behavior across four time-points. This particular outcome measure was a single item that required respondents to indicate how many cigarettes they have smoked on average each day for the past 30 days. Given that the Raftery-Lewis diagnostic is not available in Mplus to determine the proper number of burn-in and post-burn-in iterations, an initial model was computed with an arbitrary 250,000 burn-in and 250,000 post-burn-in. Then the Brooks, Gelman, and Rubin diagnostic was used to assess whether chain convergence was obtained.
GCM results
For the linear GCM examining growth rates of cigarette use across teenage years, Markov chain convergence was determined based on the Brooks, Gelman, and Rubin convergence diagnostic. Further, Figure 8 presents convergence (or history) plots and density plots of the posterior distribution extracted from the Mplus program for the intercept and slope parameters. The convergence plots for the intercept and slope are split by a threshold in the middle, where the left side of the threshold represents the burn-in phase and the right side of the threshold represents the post-burn-in phase. Note how the chain appears to have converged (even without accounting for the large the burn-in phase) based on visual inspection given that there is a relatively tight, horizontal band representing the chain. The density plots for the posterior distributions produced for the intercept and slope parameters both appear to be relatively normally distributed. If convergence had not been obtained, these densities would likely be quite lumpy and not represent smooth, normal densities; in this case, another model with a larger burn-in could be estimated to obtain convergence.
Figure 8. Convergence and posterior density plots for linear growth curve modeling assessing growth rates in cigarette use. MCMC = Markov chain Monte Carlo.
Table 2 presents the estimates for the intercept and slope terms for this linear GCM examining change in cigarette smoking across teenage years and Figure 9a illustrates the estimated trajectory. The growth trajectory indicated that overall these teenagers had an initial smoking level of 6.43 cigarettes per day in the last 30 days. Likewise, the rate of change in smoking prevalence over the course of 4 years indicated that, on average, these teenagers smoked an additional 2.04 cigarettes per day each year.
Select Model Parameter Estimates for Six Empirical Examples
GMM results
A GMM examining growth rates of cigarette usage for two latent (unobserved) classes was estimated. Results indicated that convergence was obtained based on the Brooks, Gelman, and Rubin convergence diagnostic. All parameters yielded convergence and posterior density plots that indicated convergence was obtained. Results for this analysis indicated that two substantively different classes were present. Table 2 contains the results for the intercept and slope model parameters for this analysis. Overall, Class 1 (85.8%) can be described as the low-smoking group. Specifically, the number of cigarettes smoked per day at the first wave of data collection for Class 1 was 4.23, with an average growth of 2.48 cigarettes per day each year thereafter. Class 2 (14.2%) can be described as the high-smoking group given that the average number of cigarettes smoked per day at the first wave of data collection was 17.90. Interestingly, Class 2 showed a slight negative slope (although not significant) indicating that the amount of cigarettes smoked did not change significantly across time for Class 2. Note that the GMM results are depicted in Figure 9a.
Quadratic Growth
The second set of examples illustrates the application of a quadratic growth model in the context of GCM and a two-class GMM using the Bayesian framework. The repeated measures outcome for this set of quadratic growth examples is a measure of alcohol consumption. Specifically, a single-item was used that asked respondents how many days in the past 30 days they had consumed one or more alcoholic beverages. This set of examples used the Bayesian estimation framework within the Mplus software program with the same number of burn-in and post-burn-in iterations as specified in the linear example presented above. Likewise, default diffuse priors were specified akin to the linear growth modeling example and all priors can be found in the online supplemental material and Table 1.
GCM results
For the quadratic GCM examining growth rates of alcohol consumption across early teenage years, Markov chain convergence was obtained via the Brooks, Gelman, and Rubin convergence diagnostic. The convergence and posterior density plots for the model parameters all indicated chain convergence. Table 2 and Figure 9b present the estimates for the intercept and slope terms for this quadratic GCM. The growth trajectory indicated that, on average, the teenagers drank at least one alcoholic beverage on 2.19 days in the last 30 days at the first wave of data collection. The growth trajectory indicated that there was a positive linear slope, with a slight quadratic curve that showed a leveling off and eventual decrease in alcohol consumption over time.
GMM results
For the two-class GMM depicted in Figure 9b, results indicated that convergence was obtained based on the Brooks, Gelman, and Rubin convergence diagnostic. All parameters yielded convergence plots and posterior density plots that indicated convergence was obtained. Two substantively different latent classes were produced with respect to alcohol consumption growth over time within the teenage years. Table 2 indicated that Class 1 (8.3%) represented a group of teenagers that had relatively higher levels of alcohol consumption with an average of 13.06 days where at least one alcoholic beverage was consumed in the last 30 days at the first wave of data collection. Class 1 exhibited a negative linear slope and a positive quadratic term. On average, the teenagers in Class 1 showed a decrease in alcohol consumption to a certain degree and then the consumption leveled off with an eventual increase. Class 2 (91.7%) had relatively lower rates of alcohol consumption in the initial wave, with an average of consuming one or more alcoholic beverages only 1.20 days during the last 30 days. Further, Class 2 experienced a steady increase in drinking over time with a positive slope and then a leveling off due to the negative quadratic term.
Informative priors
While frequentist estimation is based on the likelihood of the data, Bayesian estimation uses both the data likelihood and prior information regarding plausible values for the parameter. This additional component of inference, the prior, can contain no information (equivalent to maximum likelihood), full information (akin to a fixed parameter), and everything in between (in the form of probability distributions). This flexibility in allowing for prior information is what separates Bayesian from frequentist statistics and is a potential advantage of Bayesian inference.
To demonstrate the use of informative priors with one of our examples, we analyzed a separate sample of equal size (N = 500) from the same database. Results from this analysis are used to inform the priors in a new analysis of the original sample. While this is technically a data-driven technique for defining priors (akin to Gelman et al., 1996), it is meant to demonstrate the use of results from a previous study. Specifically, a second sample was pulled and a GMM was estimated using maximum-likelihood estimation. The estimates and standard deviations produced for the intercept, slope, and quadratic terms were then used as the mean and variance hyperparameter values for informative priors specified in a model estimated using the original sample. For example, the Class 1 intercept in the new sample (under maximum likelihood) was 18.29 compared to 13.06 in the original sample with noninformative priors. When 18.29 was used to inform the mean of the informative prior (see Table 1 for all priors used), the posterior mean for the Class 1 intercept was 13.71 (see Table 2). The effect of the informative prior can be seen in the change of the intercept (i.e., the new mean has been pulled upward by the informative prior), though in this case the substantive interpretation remains the same.
Logistic Growth
The final set of examples shows the application of logistic growth over time via GCM and a two-class GMM. The repeated measures outcome used here was a measure of depression. Specifically, this outcome was a single-item measure that asked respondents how often they had been depressed in the last month. The response was measured using a 4-point Likert-type scale where higher values were associated with lower levels of depression; to promote clarity, it should be noted that this coding is reversed from the hypothetical depression example above. Measurements occurred once every 2 years for 5 years starting in 2000. The first four waves of data were used for the first 500 respondents with complete data to stay consistent with the previous examples.
Although a logistic curve can be parameterized in a number of ways, a logistic model with four growth parameters is used here. The parameters, which varied across all respondents in the models included: the lower asymptote of the logistic curve (i.e., floor parameter), the amount of change from the lower to upper asymptote (i.e., change parameter), the slope, and the point that is half way between the lower and upper asymptotes (i.e., inflection point). A general picture of a labeled logistic growth model can be seen in Figure 9c.
The Bayesian logistic growth models were estimated using the freely available program OpenBUGS (Version 3.2.1). These examples required the use of OpenBUGS since Bayesian logistic growth models are not yet available in Mplus. OpenBUGS does not have a built-in monitoring system for chain convergence, so the BOA package within R was used to examine chain convergence based on chain information from the CODA files obtained from OpenBUGS; the online supplemental material provides full details for implementing this package in R.
Chain convergence was assessed via visual inspection of the convergence plots as well as through three different diagnostics: Raftery-Lewis, Geweke, and Heidelberger and Welch. An initial model was estimated using one million iterations in the chain and no burn-in. Upon loading chain information into the BOA package in R, the Raftery-Lewis diagnostic indicated that a burn-in of about 20,000 would be sufficient. However, during examination of the convergence plots, it was visually clear that the portion of the chain representing nonconvergence was much larger than 20,000. Likewise, the Geweke and Heidelberger and Welch diagnostic criteria indicated that many of the model parameters had not converged (e.g., growth parameters and variance parameters). A much larger burn-in phase of 500,000 was used, with a post-burn-in phase of 500,000 iterations. Even with such a large number of iterations, the Geweke and Heidelberger and Welch diagnostics still indicated that some of the variance model parameters (e.g., the variance for the floor effect) were not fully converged. After a sensitivity analysis of examining different chain lengths, it was determined that convergence for these parameters would likely not be obtained. The reason for this is likely that only about 24% of the individuals followed a logistic growth pattern and the remaining individuals followed a linear growth pattern. However, for pedagogical purposes, this example was still included to demonstrate the implementation of Bayesian logistic growth modeling.
GCM/GMM results
Parameter estimates for the logistic growth model within GCM and GMM are presented in Table 2. However, it may be more informative to view the plot presented in Figure 9d, which illustrates the growth pattern for the GCM (solid line) and the growth patterns for GMM (dashed and dotted lines). Specifically, GCM results do not appear to be visually logistic in nature across these four waves of data. Likewise, Class 2 under GMM also appears to be rather linear. However, Class 1 under GMM does show the classic logistic pattern where there is a clear floor and a clear ceiling for this growth curve. This indicates that Class 1 (the minority class with 23.5% of the cases) showed that overall individuals had higher initial depression levels (represented by lower scores on the outcome according to the NLSY coding scheme), and these depression levels decreased in a nonlinear fashion over time.
Credibility intervals
In the Bayesian framework, inferences about a parameter are made using the parameter’s posterior distribution (i.e., probability distribution of plausible parameter values given the data and prior information). While it is common to use point estimates from this distribution, such as the mean or median, to make inferences about a parameter, we are often interested in probability intervals for a given parameter. For any given sample, we obtain the sample mean and form a 100(1 − α)% confidence interval. An α level of .05 therefore results in a 95% confidence interval. In a Bayesian framework, these intervals are called credibility intervals or posterior probability intervals and reflect the probability that a parameter lies within the given interval. In other words, a 95% credibility interval means that the probability that the parameter lies in the interval is 95%. The advantage of using Bayesian credibility intervals as opposed to conventional confidence intervals has to do with assumption and interpretation. Because parameters are treated as random variables in the Bayesian framework, these intervals can be interpreted as the probability of the parameter lying within that interval, an interpretation that is much more intuitive compared to the interpretation of a confidence interval. Additionally, credibility intervals make no assumptions about the normality or symmetry of the posterior distribution, unlike confidence intervals that assume the sampling distribution is normal and therefore also symmetric.
A demonstration of making inferences with credibility intervals will be made using the slope parameter of the logistic model for Class 1 under GMM. This parameter captures the rate of change (i.e., slope) in the outcome at the inflection point and the asymmetrical posterior distribution can be seen in Figure 10. Ninety-five percent of the slope parameter’s posterior distribution for Class 1 is between 3.68 and 154.20 with a median of 34.94 (note the asymmetry in this interval). The interpretation is that there is a 95% chance that the population slope parameter for Class 1 is between 3.68 and 154.20. The potential advantage of using credibility intervals is underscored by the fact that the estimated posterior distribution for this parameter is skewed and not normal. The credibility interval contains information from an empirically derived distribution as opposed to a frequentist confidence interval that is based on large sample theory, thus interpreting a credibility interval is much more informative and accurate compared to a traditional confidence interval.
Figure 10. Posterior density for the logistic growth mixture modeling depression slope parameter for Class 1 to illustrate credibility intervals. Note that the 95% credibility interval is (3.679, 154.20), and this is not symmetric surround the estimate, which was 47.02; this contrasts symmetric frequentist confidence intervals.
Concluding RemarksThe examples above demonstrate that growth models can be effectively fit using Bayesian methods for estimation via MCMC. As mentioned in the introduction, the greatest appeal of using Bayesian methods for modeling growth is when change over time is nonlinear or when sample sizes are relatively smaller (e.g., n = 150). In these cases, modeling accuracy (e.g., model estimate accuracy) may benefit from the use of prior information or knowledge being integrated into the estimation process.
However, this alternative method implementing Bayesian estimation also presents one main challenge of which the practitioner should be aware. The most notable challenge with respect to the growth models discussed here is ironically also rooted in one of the main advantages or benefits of the estimation framework: namely, the specification of prior distributions. A common concern when implementing the Bayesian framework is whether the prior distributions specified are accurate. If the prior knowledge being integrated into the estimation process is completely wrong, then one concern is that these priors will produce inaccurate estimates–especially if sample sizes are small. Depaoli (in press) specifically examined the impact of inaccurate priors within the context of growth via Monte Carlo (simulation) techniques. Results indicated that many of the negative effects of an inaccurate prior could be combated by simply increasing the size of the variance hyperparameter.
Practitioners that are new to Bayesian methods may find it useful to first experiment with fitting simple growth models (e.g., GCM with linear growth) before expanding into more complex growth models (e.g., GMM with logistic growth). This sequential modeling-building method will allow the practitioner to become familiar with the basic features of Bayesian estimation before branching out into fitting more complex models that require the implementation of more advanced techniques.
Bayesian methods are not only useful in the context of modeling growth. In fact, many other models (e.g., path analysis or regression) commonly implemented within the clinical research setting can also benefit from some of the features of the Bayesian estimation framework. For a detailed description of the use of Bayesian methods in broader modeling contexts (e.g., the broader structural equation modeling framework), see the recent series of articles published in Psychological Methods on this topic: B. Muthén and Asparouhov (2012a); B. Muthén and Asparouhov (2012b); MacCallum, Edwards, and Cai (2012); and Rindskopf (2012). This series specifically highlights estimation issues that arise under conventional estimation methods and also addresses Bayesian estimation in a broad cross-sectional modeling context.
It is our hope that clinical researchers (and akin) will find these tools helpful in fitting a variety of models more accurately, which include models capturing growth or change over time. Tutorial guides are available as online supplemental material with instructions for easily implementing Bayesian methods in the Mplus software and the freely available R and OpenBUGS (or WinBUGS) programming environments.
Footnotes 1 Note that in an attempt to index common sample sizes found in clinical research using GCM and GMM, the median sample size for the last 5 years of publications in the Journal of Consulting and Clinical Psychology was computed. For studies employing GCM, the median sample size was 177, and the median for studies implementing GMM was 504.
2 Note (informative) priors are defined later in this section.
3 Prior distributions can take the form of many different probability distributions (e.g., gamma, Wishart, and Poisson distributions). For the purposes of this article, the normal and uniform distributions will be used as the priors specified for the different growth model parameters (e.g., the intercept and linear slope parameters), and the Dirichlet prior will be used for latent class proportions.
4 Note that noninformative and diffuse are typically used interchangeably to represent a prior that does not place preference over a particular range of values.
5 One possible method for decreasing the dependence among samples is to employ a method called thinning. When a chain is thinned, then only every sth sample is selected to comprise the posterior. In other words, if a thinning interval of 5 was used, then every fifth sample would be used to form the posterior distribution. This process can help to reduce dependence on prior samples.
6 It should be noted here that Figure 7 contains examples of different forms of (non)convergence in a chain. These three examples represent three different parameters that are completely unconnected to the GCM and GMM examples provided in this article. Figure 7 is meant to visually depict (non)convergence, but this figure is not meant for any sort of substantive interpretation outside of this section discussing MCMC convergence.
7 A stationary test essentially tests to see whether the mean and variance of the chain remains consistent across the entire length of the post burn-in phase. Nonstationarity would indicate that there was a fluctuation in the mean and/or the variance throughout the chain.
8 In the multiple-chain case, each chain samples from another location of the posterior distribution based on purposefully disparate starting values. With multiple chains it may be the case that fewer iterations are required if there is evidence that each chain has converged upon the same posterior mean.
9 All of the examples presented below use only a single chain so when the Brooks, Gelman, and Rubin diagnostic is discussed below, it is implied that the PSR factor was composed of the first and last portions of a single chain.
10 It is important to note that in applied settings, one does not know the true class proportions, thus, this class-size break-down should primarily be viewed as an illustration.
11 Convergence plots and posterior density plots for model parameters for the remaining examples are available upon request from the first author.
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Submitted: October 31, 2012 Revised: September 26, 2013 Accepted: October 7, 2013
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Source: Journal of Consulting and Clinical Psychology. Vol. 82. (5), Oct, 2014 pp. 784-802)
Accession Number: 2013-44747-001
Digital Object Identifier: 10.1037/a0035147
Record: 28- Title:
- Low social rhythm regularity predicts first onset of bipolar spectrum disorders among at-risk individuals with reward hypersensitivity.
- Authors:
- Alloy, Lauren B.. Department of Psychology, Temple University, Philadelphia, PA, US, lalloy@temple.edu
Boland, Elaine M.. Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, US
Ng, Tommy H.. Department of Psychology, Temple University, Philadelphia, PA, US
Whitehouse, Wayne G.. Department of Psychology, Temple University, Philadelphia, PA, US
Abramson, Lyn Y.. Department of Psychology, University of Wisconsin, WI, US - Address:
- Alloy, Lauren B., Department of Psychology, Temple University, 1701 North 13th Street, Philadelphia, PA, US, 19122, lalloy@temple.edu
- Source:
- Journal of Abnormal Psychology, Vol 124(4), Nov, 2015. pp. 944-952.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 9
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- social rhythm regularity, social zeitgebers, reward sensitivity, bipolar spectrum disorders
- Abstract (English):
- The social zeitgeber model (Ehlers, Frank, & Kupfer, 1988) suggests that irregular daily schedules or social rhythms provide vulnerability to bipolar spectrum disorders. This study tested whether social rhythm regularity prospectively predicted first lifetime onset of bipolar spectrum disorders in adolescents already at risk for bipolar disorder based on exhibiting reward hypersensitivity. Adolescents (ages 14–19 years) previously screened to have high (n = 138) or moderate (n = 95) reward sensitivity, but no lifetime history of bipolar spectrum disorder, completed measures of depressive and manic symptoms, family history of bipolar disorder, and the Social Rhythm Metric. They were followed prospectively with semistructured diagnostic interviews every 6 months for an average of 31.7 (SD = 20.1) months. Hierarchical logistic regression indicated that low social rhythm regularity at baseline predicted greater likelihood of first onset of bipolar spectrum disorder over follow-up among high-reward-sensitivity adolescents but not moderate-reward-sensitivity adolescents, controlling for follow-up time, gender, age, family history of bipolar disorder, and initial manic and depressive symptoms (β = −.150, Wald = 4.365, p = .037, odds ratio = .861, 95% confidence interval [.748, .991]). Consistent with the social zeitgeber theory, low social rhythm regularity provides vulnerability to first onset of bipolar spectrum disorder among at-risk adolescents. It may be possible to identify adolescents at risk for developing a bipolar spectrum disorder based on exhibiting both reward hypersensitivity and social rhythm irregularity before onset occurs. (PsycINFO Database Record (c) 2018 APA, all rights reserved)
- Impact Statement:
- General Scientific Summary—The tendency to maintain irregular daily activity schedules predicts first onset of bipolar spectrum disorder in adolescents with high sensitivity to rewards. (PsycINFO Database Record (c) 2018 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Bipolar Disorder; *Onset (Disorders); *Social Processes; Rewards
- Medical Subject Headings (MeSH):
- Adolescent; Bipolar Disorder; Female; Humans; Male; Prospective Studies; Reward; Risk Factors; Social Behavior; Young Adult
- PsycINFO Classification:
- Affective Disorders (3211)
- Population:
- Human
- Location:
- US
- Age Group:
- Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Behavioral Inhibition System/Behavioral Activation System Scales
Sensitivity to Punishment Sensitivity to Reward Questionnaire
Schedule for Affective Disorders and Schizophrenia–Lifetime
Altman Self-Rating Mania Scale DOI: 10.1037/t64284-000
Beck Depression Inventory DOI: 10.1037/t00741-000 - Grant Sponsorship:
- Sponsor: National Institute of Mental Health, US
Grant Number: MH77908 and MH102310
Recipients: Alloy, Lauren B.
Sponsor: Office of Academic Affiliations, Department of Veterans Affairs, US
Other Details: Advanced Fellowship Program in Mental Illness Research and Treatment
Recipients: No recipient indicated - Methodology:
- Empirical Study; Longitudinal Study; Prospective Study; Interview; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Aug 12, 2015; Revised: Aug 9, 2015; First Submitted: Jan 23, 2015
- Release Date:
- 20151123
- Correction Date:
- 20180215
- Copyright:
- American Psychological Association. 2015
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/abn0000107
- PMID:
- 26595474
- Accession Number:
- 2015-52362-009
- Number of Citations in Source:
- 54
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-52362-009&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-52362-009&site=ehost-live">Low social rhythm regularity predicts first onset of bipolar spectrum disorders among at-risk individuals with reward hypersensitivity.</A>
- Database:
- PsycINFO
Low Social Rhythm Regularity Predicts First Onset of Bipolar Spectrum Disorders Among At-Risk Individuals With Reward Hypersensitivity
By: Lauren B. Alloy
Department of Psychology, Temple University;
Elaine M. Boland
Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania, and Department of Psychiatry, University of Pennsylvania School of Medicine
Tommy H. Ng
Department of Psychology, Temple University
Wayne G. Whitehouse
Department of Psychology, Temple University
Lyn Y. Abramson
Department of Psychology, University of Wisconsin
Acknowledgement: This research was supported by National Institute of Mental Health Grants MH77908 and MH102310 to Lauren B. Alloy. The writing of this article also was supported by the Office of Academic Affiliations, Advanced Fellowship Program in Mental Illness Research and Treatment, Department of Veterans Affairs.
Disruption of circadian rhythms has been proposed to be a central mechanism in the neurobiological vulnerability to bipolar spectrum disorders (BSDs; McClung, 2007; Murray & Harvey, 2010). According to the social zeitgeber theory of mood disorders (Ehlers, Frank, & Kupfer, 1988; Grandin, Alloy, & Abramson, 2006), life events that disrupt social zeitgebers, defined as daily social rhythms or schedules (e.g., mealtimes, bedtimes, start and end of work), are hypothesized to disturb circadian rhythms, which, in turn, precipitate bipolar symptoms. Events that perturb social rhythms (e.g., causing a change in bedtime, skipping a meal) may disrupt circadian rhythms through their effects on either photic (e.g., Roenneberg & Merrow, 2007; Stetler, Dickerson, & Miller, 2004; Wever, 1989) or nonphotic (e.g., Goel, 2005) cues that help to entrain circadian rhythms. Changes in daily activity patterns, whether manipulated or naturally occurring, have been found to be associated with changes in social rhythms in healthy individuals (e.g., Stetler et al., 2004).
A growing body of evidence supports the social zeitgeber model of BSDs (see Alloy, Nusslock, & Boland, 2015, for a review). For example, studies have found significant associations between life-event-induced social rhythm disruption and episode onset in individuals with BSDs. Malkoff-Schwartz and colleagues (1998, 2000) reported that more social rhythm disrupting events occurred in the period prior to manic episode onset than in a matched, equal duration control period not related to episode onset in a sample with bipolar I disorder. In addition, Sylvia et al. (2009) found that the occurrence of social rhythm disrupting events significantly predicted prospective onset of depressive episode recurrences in a sample of individuals with bipolar II disorder or cyclothymia. Moreover, Boland and colleagues (2012) found that individuals with BSDs exhibit an underlying sensitivity to life-event-induced social rhythm disruption. Compared with demographically matched healthy controls, individuals with BSDs experienced significantly more social rhythm disruption following the experience of similar intensity and valence life events (Boland et al., 2012).
Given the hypothesized role of social zeitgebers in entraining biological rhythms, individuals with low social rhythm regularity may be at increased risk for desynchronization of circadian rhythms and, in turn, onset of bipolar mood episodes. That is, low social rhythm regularity should serve as a vulnerability to bipolar mood episodes. Several studies have used the Social Rhythm Metric (SRM; Monk, Flaherty, Frank, Hoskinson, & Kupfer, 1990) to compare individuals with BSDs and controls on self-reported regularity of their social rhythms. The SRM assesses the frequency (number of days per week) with which daily activities (e.g., getting out of bed, eating lunch, starting work or school) are performed and the degree of regularity (occurrence of each activity within 45 min of the average time) of these activities. Social rhythm regularity scores are calculated as the number of activities that occur at least three days per week within 45 min of the average time. Consistent with the social zeitgeber model, studies using different versions of the SRM find that individuals with BSDs exhibit lower social rhythm regularity than do healthy controls (Ashman et al., 1999; Jones, Hare, & Evershed, 2005; Szuba, Yager, Guze, Allen, & Baxter, 1992), even during the euthymic state (Jones et al., 2005; Shen, Alloy, Abramson, & Sylvia, 2008). And in euthymic individuals with bipolar II or cyclothymia diagnoses, low social rhythm regularity scores on a modified SRM at baseline predict a greater likelihood and shorter time to recurrence of both major depressive and hypomanic or manic episodes over prospective follow-up, controlling for baseline subsyndromal mood symptoms and family history of bipolar disorder (Shen et al., 2008).
Consistent with the hypothesis that low social rhythm regularity may confer vulnerability to BSDs, individuals at behavioral risk for bipolar disorder based on exhibiting hypomanic personality (Meyer & Maier, 2006) or subsyndromal bipolar symptoms (Bullock, Judd, & Murray, 2011) exhibited lower social rhythm regularity on a brief version of the SRM (SRM-5) than did participants at low behavioral risk for bipolar disorder. Alternatively, in a genetic high-risk study, Jones, Tai, Evershed, Knowles, and Bentall (2006) did not find lower social rhythm regularity on the SRM in the children of bipolar parents compared with the age- and sex-matched children of healthy control parents. It is possible that Jones et al. (2006) did not observe lower social rhythm regularity in the children of bipolar parents because children’s daily activity schedules are more regimented by their parents, school, and other external constraints. Thus, there is some, but not fully consistent, evidence for low social rhythm regularity in individuals without a current bipolar disorder but who are at behavioral risk for developing a BSD. However, no study to date has examined whether low social rhythm regularity prospectively predicts first lifetime onset of BSD in individuals at behavioral risk for bipolar disorder, which would provide the strongest evidence for irregular social rhythms as a vulnerability factor for BSDs. This is the goal of the present study.
We examined social rhythm regularity as a predictor of first onset of BSD in a group of adolescents previously shown to be at increased risk for BSD based on exhibiting a hypersensitive reward system, and a comparison group of adolescents at low risk for BSD based on exhibiting moderate reward sensitivity. According to the reward hypersensitivity theory of bipolar disorder (e.g., Alloy & Abramson, 2010; Alloy et al., 2015; Johnson, Edge, Holmes, & Carver, 2012; Urošević, Abramson, Harmon-Jones, & Alloy, 2008), vulnerability to BSDs is hypothesized to be the result of an overly sensitive reward system that is hyperreactive to goal- and reward-relevant cues. Much self-report, behavioral, life-event, neurophysiological, and neuroimaging evidence supports the reward hypersensitivity theory of BSDs (see Alloy et al., 2015, for a recent review). Consistent with the reward model, Alloy et al. (2012) previously demonstrated that, controlling for baseline hypomanic and depressive symptoms and family history of bipolar disorder, adolescents who exhibited high levels of reward sensitivity at baseline, but had no prior history of BSD, were significantly more likely to develop a first onset of a BSD over an average of 13 months of prospective follow-up than were adolescents with moderate reward sensitivity. Although high-reward-sensitive adolescents were 3 times more likely to develop a BSD than moderate-reward-sensitive adolescents (12.3% vs. 4.2%), the majority of the at-risk reward-hypersensitive adolescents did not develop a BSD over the follow-up period (Alloy et al., 2012). Thus, it is important to examine additional factors, such as low social rhythm regularity, considered to be a vulnerability to BSD from the perspective of the social zeitgeber model, which may further predict which individuals will go on to develop first onset of BSD.
Moreover, the social and circadian rhythm disruption and reward hypersensitivity models of BSD can be integrated involving bidirectional associations between reward sensitivity and social and circadian rhythm dysregulation (e.g., Alloy et al., 2015; Murray et al., 2009; Nusslock, Abramson, Harmon-Jones, Alloy, & Coan, 2009). Indeed, there is evidence of circadian influences on reward motivation (e.g., Clark, Watson, & Leeka, 1989; Murray et al., 2009; Thayer, Takahashi, & Pauli, 1988), associations between reward-related brain activity and circadian clock genes (Forbes et al., 2012) and sleep variables (Holm et al., 2009), as well as reward hypersensitivity influences on social rhythm disruption (Boland et al., in press). These bidirectional influences of the reward and circadian systems suggest that examining social rhythm regularity as a predictor of first onset of BSD among adolescents with high reward sensitivity may prove fruitful and increase the precision of identification of adolescents at increased risk for first onset of BSD. In turn, early identification of adolescents at greatest risk for onset of BSD is important for early intervention efforts designed to prevent onset or mitigate the severity of BSD.
Consequently, the present study provides the first test of social rhythm regularity as a predictor of first lifetime onset of BSD in a sample of adolescents at increased risk for BSD based on exhibiting reward hypersensitivity, and a comparison group of low-risk adolescents with moderate reward sensitivity. Consistent with the social zeitgeber model of BSDs, we hypothesized that among high-reward-sensitive, but not moderate-reward-sensitive, adolescents with no prior history of BSD, lower levels of social rhythm regularity would predict a greater likelihood of developing a first onset of BSD over prospective follow-up than would higher social rhythm regularity.
Method Participants
Participants in the current study were a subset of those participating in the Teen Emotion and Motivation (TEAM) Project, a prospective longitudinal study of predictors of first onset of BSD (Alloy et al., 2012). All participants <18 years old provided written assent and their parents provided written consent, whereas participants ≥18 years old provided their own written consent. All procedures were approved by the Temple University Institutional Review Board. Adolescents (ages 14–19 years) were selected for Project TEAM based on a two-phase screening procedure. The age range of 14 to 19 was selected because it is a major “age of risk” for first onset of BSD (see Alloy et al., 2012 for review). In Phase I, students from 13 Philadelphia public high schools (Grades 9−12, ages 14–18) and two universities (ages 17–19) were screened with two self-report reward sensitivity questionnaires: the Behavioral Inhibition System/Behavioral Activation System Scales (BIS/BAS; Carver & White, 1994) and the Sensitivity to Punishment Sensitivity to Reward Questionnaire (SPSRQ; Torrubia, Avila, Molto, & Caseras, 2001). Students scoring in the highest 15th percentile on both the BAS-Total subscale of the BIS/BAS Scales and the Sensitivity to Reward (SR) subscale of the SPSRQ formed a High BAS (reward sensitivity) group, whereas those who scored between the 40th and 60th percentiles on both measures, formed the moderate-BAS (reward sensitivity) group. Of 9,991 students screened in Phase I, 7.77% (n = 776) qualified for the high-BAS group and 4.04% (n = 404) qualified for the moderate-BAS group.
A subset of adolescents who met the Phase I screening criteria was invited for Phase II screening (see Alloy et al., 2012, for more details on Phase II selection of participants): 244 high-BAS (31.4%) and 146 moderate-BAS (36.1%) students completed Phase II. In Phase II, participants were administered the mood and psychosis disorder sections of an expanded Schedule for Affective Disorders and Schizophrenia–Lifetime (exp-SADS-L) diagnostic interview (Alloy et al., 2008; Endicott & Spitzer, 1978); the rest of the exp-SADS-L, including a family history section, was administered to the final eligible sample at baseline (Time 1). Participants also completed the Beck Depression Inventory (BDI; Beck, Rush, Shaw, & Emery, 1979) and the Altman Self-Rating Mania Scale (ASRM; Altman, Hedeker, Peterson, & Davis, 1997) at Phase II screening to assess depressive and (hypo)manic symptoms, respectively. Exp-SADS-L interviewers were blind to participants’ BAS risk group.
Participants were excluded from the final sample if they met Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; DSM–IV–TR; American Psychiatric Association, 2000) or Research Diagnostic Criteria (RDC; Spitzer, Endicott, & Robins, 1978) diagnosis of (a) any BSD (bipolar I, bipolar II, cyclothymia, bipolar not otherwise specified [NOS]) or a hypomanic episode with onset prior to the participant’s Phase I screening date, or (b) any lifetime psychotic disorder (schizophrenia, schizoaffective disorder, major depressive disorder with psychosis). They were not excluded if they met criteria for a nonpsychotic DSM–IV–TR or RDC major depressive or RDC minor depressive episode with onset prior to Phase I, because prior depressive episodes without mania or hypomania may reflect unipolar depression rather than bipolar disorder. Participants were also excluded if they lacked fluency in English. Participants with a prior BSD or hypomanic episode were excluded because the main goal of Project TEAM was to examine predictors of first onset of BSD.
Of 390 participants interviewed at Phase II, 22 were excluded because they met criteria for a BSD or hypomanic episode with onset prior to their Phase I screening, seven were excluded because they exhibited psychotic symptoms or met criteria for a psychotic disorder, and another five were excluded for poor English fluency. The Project TEAM final sample included 171 high-BAS and 119 moderate-BAS participants (mean age = 17.44 years; SD = 1.56). Further details of the screening and selection criteria and evidence that the final sample was representative of both the Phase I and Phase II screening samples may be found in Alloy et al. (2012).
The present analyses were based on only the participants who also had baseline (Time 1) social rhythm regularity data needed for the current study. Thirty-three of the 171 high-BAS participants, and 24 of the 119 moderate-BAS participants, in the final TEAM sample were missing Time 1 social rhythm regularity data; thus, the present analyses were based on 138 (87 female, 51 male) high-BAS participants and 95 (68 female, 27 male) moderate-BAS participants, with mean ages at baseline of 18.11 and 17.87 years (SDs = 1.49 and 1.63), respectively. The racial breakdown of the sample was 54.2% White, 30.2% African American, 8.0% Asian or Pacific Islander, 4.5% Biracial, and 3.1% Other. In addition, 8.5% were Hispanic. A family history of bipolar disorder was present in 6% of the high-BAS and 11% of the moderate-BAS participants. The participants with missing social rhythm regularity data did not differ from those included on demographics, initial BAS and SR scores, or initial BDI and ASRM scores. In addition, the high-BAS and moderate-BAS groups did not differ from each other on age, gender, or race/ethnicity. Table 1 presents means and standard deviations of the baseline BAS Total, SR, BDI, ASRM, and SRM regularity scores.
Means and Standard Deviations of Baseline Measures
Although Boland et al. (in press) also studied the interplay between reward sensitivity and social rhythm dysregulation in the Project TEAM sample, there is no overlap between the Boland et al. and current studies. Whereas Boland et al. used interviewer-rated social rhythm disruption scores in response to actual life events that high-BAS and moderate-BAS participants experienced at the first follow-up assessment to predict subsequent hypomanic and depressive symptoms, the present study uses self-reported trait social rhythm regularity at baseline on the SRM to predict first onset of diagnosed BSDs.
Procedure
Participants in the final sample were invited for a baseline (Time 1) assessment and the prospective study, and additional informed consent and assent were obtained at Time 1. At Time 1, participants completed the remainder of the exp-SADS-L diagnostic interview, including family history and substance use disorders sections, and a modified SRM (Monk et al., 1990) as well as other measures not relevant to the present study. Then, they completed an exp-SADS-Change (exp-SADS-C) diagnostic interview (Alloy et al., 2008; Spitzer & Endicott, 1978) approximately every 6 months. The present analyses were based on an average of 31.7 months (SD = 20.1 month) of prospective follow-up.
Measures
BIS/BAS Scale
The BIS/BAS Scale (Carver & White, 1994) is a 20-item, self-report questionnaire that assesses trait individual differences in BIS and BAS sensitivities in general. It includes three BAS subscales and one BIS subscale and items are rated on 4-point Likert scales ranging from 1 = strongly disagree to 4 = strongly agree. A BAS-Total score also may be calculated as the sum of all BAS items. This total score was used as part of the screening criteria for selecting the BAS risk groups. The BIS/BAS scales have demonstrated internal consistency and retest reliability (Carver & White, 1994), as well as construct validity, exhibiting expected associations with affect, personality traits, and performance on reaction time and learning tasks involving incentives (Colder & O’Connor, 2004; Kambouropoulos & Staiger, 2004; Zinbarg & Mohlman, 1998). Internal consistency of the BAS-Total score in the Phase I screening sample was α = .80.
SPSRQ
The SPSRQ (Torrubia et al., 2001) is another measure of trait BIS and BAS sensitivities in general that focuses on sensitivity to specific types of rewards and punishments. It contains 24 SR (e.g., “Does the good prospect of obtaining money motivate you strongly to do some things?”) and 24 Sensitivity to Punishment (SP; e.g., “Do you often refrain from doing something because you are afraid of it being illegal?) “yes” or “no” items. Both subscales have acceptable internal consistency, with alphas of .75 to .83, and retest reliabilities (Torrubia et al., 2001). The SPSRQ also exhibits construct validity in terms of expected correlations with extraversion, impulsivity, sensation seeking, and neuroticism, and associations with proneness to various personality disorders (e.g., Alloy et al., 2006; Torrubia et al., 2001). The SR subscale was used along with the BAS-T to select high-BAS versus moderate-BAS participants. In Phase I, alpha for the SR scale was .76. BAS-T and SR scores correlated r = .40 in our Phase I sample.
Modified SRM (M-SRM)
The SRM (Monk et al., 1990) assesses a person’s daily social rhythm patterns. It captures the frequency and timing of specific activities that are part of a person’s daily routine (e.g., getting out of bed, going to bed, mealtimes, first social contact of the day, starting work or school). The SRM includes 15 specified daily activities and two individualized write-in items. The SRM was found to be modestly consistent (r = .44) and valid in healthy controls (Monk et al., 1990; Monk, Kupfer, Frank, & Ritenour, 1991), and it distinguishes individuals with BSDs from controls (Ashman et al., 1999; Jones et al., 2005; Shen et al., 2008; Szuba et al., 1992) and predicts recurrences of BSD mood episodes (Shen et al., 2008). In the current study, we used a slightly modified version of the SRM (M-SRM) previously used by Shen et al. (2008). The M-SRM is intended to assess more trait-like social rhythm regularity or an individual’s typical social rhythm patterns. Participants were asked to endorse activities if they occurred a minimum of 3 times per week within 45 min of the usual time each week over the past month. The Regularity score was the number of activities endorsed as occurring three or more times within 45 min of the habitual time during the week (possible range = 0 to 17) over the past month. The M-SRM was moderately consistent over approximately eight months in a sample of late adolescents and young adults with BSD and healthy control participants (r = .61; Shen et al., 2008). The M-SRM was given at baseline (Time 1) and exhibited good internal consistency (α = .76). In addition, we calculated a Sleep/Wake Regularity score for exploratory analyses based on the go-to-bed and get-out-of-bed items.
BDI
The BDI (Beck et al., 1979) has 21 items assessing the severity of affective, cognitive, motivational, and somatic symptoms of depression over the past 30 days. It has good internal (αs = .81 to .86) and retest reliability (rs = .48 to .86) and validity in nonclinical samples (Beck, Steer, & Carbin, 1988). In our Phase II sample, α = .87.
ASRM
The ASRM (Altman et al., 1997) has five items rated on 5-point Likert scales that assess five symptoms of (hypo)mania over the past 30 days: inflated self-confidence, talkativeness, elation, reduced need for sleep, and excessive activity. Items load on a single factor and ASRM scores are highly correlated with both clinical interview and other self-report measures of mania (Altman, Hedeker, Peterson, & Davis, 2001). In our Phase II sample, α = .75.
SADS-L
The SADS-L (Endicott & Spitzer, 1978) is a semistructured diagnostic interview used to assess current and lifetime history of Axis I disorders. The mood disorders and psychosis sections of an expanded SADS-L (exp-SADS-L; see Alloy et al., 2008, 2012) were given during Phase II to determine eligibility for the final Project TEAM sample, with the remainder administered at baseline (Time 1) to participants in the final sample. The exp-SADS-L assessed the occurrence, duration, and severity of symptoms related to mood, anxiety, eating, substance use, and psychotic disorders over the course of an individual’s lifetime. Specific additions were made to the SADS-L to obtain project-specific information and included (a) additional probes to allow for DSM–IV–TR (American Psychiatric Association, 2000) as well as RDC diagnoses; (b) additional probes regarding mood episodes to better capture individual symptom differences, duration, and frequency of episodes; and (c) additional sections on eating disorders, attention-deficit hyperactivity disorder, acute stress disorder, medical history, family history, and organic rule-out conditions. Project interviewers were highly trained doctoral students in clinical psychology, postdoctoral fellows, and postbaccalaureate research assistants. They were unaware of participants’ BAS risk group and Phase I BIS/BAS and SPSRQ scores. The exp-SADS-L has demonstrated excellent interrater reliability, with κ > .90 for unipolar depression diagnoses based on 80 jointly rated interviews (Alloy et al., 2000), and κ > .96 for BSDs based on 105 jointly rated interviews (Alloy et al., 2008). Further details regarding the exp-SADS-L administration and diagnoses for Project TEAM may be found in Alloy et al. (2012).
SADS-C
An expanded version of the SADS-C (Alloy et al., 2008, 2012; Spitzer & Endicott, 1978) diagnostic interview was used to assess prospective onsets of mood episodes and diagnoses. It was administered approximately every 6 months during the prospective follow-up by interviewers blinded to participants’ BAS risk group, BIS/BAS and SPSRQ scores, SRM scores, and baseline diagnostic information and family history. The exp-SADS-C was expanded in the same way as the exp-SADS-L and also included features of the Longitudinal Interval Follow-up Evaluation (LIFE II; Shapiro & Keller, 1979) to track symptoms and mood episodes over follow-up. However, the exp-SADS-C inquired about the presence of every symptom of depression and hypomania and mania more frequently (daily) than does the LIFE II (weekly) during each 6-month follow-up interval. Based on joint ratings of 60 interviews, interrater reliability for the exp-SADS-C was κ > .80 (Alloy et al., 2008). In addition, a validity study that compared participants’ dating of symptoms on the exp-SADS-C against daily symptom ratings obtained prospectively over 4 months obtained 70% accuracy for exp-SADS-C symptom dating (Alloy et al., 2008). In Project TEAM, interrater reliability for BSDs on the exp-SADS-L or exp-SADS-C interviews was κ = 1.0 for Bipolar I, κ = .92 for Bipolar II, and κ = .88 for Bipolar NOS.
Data Analysis
To examine whether social rhythm regularity on the M-SRM at baseline predicted the likelihood of first onset of a BSD among high-BAS as well as moderate-BAS participants, we conducted a hierarchical logistic regression analysis for each group with the occurrence (yes–no) of a BSD during follow-up as the dependent variable. In each logistic regression, the length of follow-up (in days), gender, age at baseline, baseline depressive (BDI) and hypomanic (ASRM) symptoms, and family history of bipolar disorder were entered in Step 1 as covariates. Then, baseline M-SRM regularity scores were entered in Step 2. We included age and baseline depressive and (hypo)manic symptoms as covariates to control for any effects of initial differences in age and subsyndromal mood symptoms on the prospective first onset of a BSD. We also included family history of bipolar disorder as a covariate to ensure that any prediction of first onset of BSD by social rhythm regularity was above and beyond any effects of family history. Subsequently, if social rhythm regularity was a significant predictor of BSD onset in either group, we reconducted the relevant logistic regression analysis controlling for further possible confounds (i.e., BAS-Total scores, presence of alcohol or drug use disorders). We also explored the role of regularity of sleep and wake times specifically in accounting for any social rhythm regularity effects.
ResultsOf the 138 high-BAS and 95 moderate-BAS participants, 20 high-BAS and four moderate-BAS individuals developed a first onset of BSD during follow-up. Of these 24 BSD-onset cases, 15 had bipolar II (onset of at least one hypomanic and one major depressive episode or onset of a hypomanic episode) and nine had bipolar NOS (onset of at least one hypomanic episode). The mean age of first onset of BSD was 20.5 years (SD = 2.8 years). Table 2 displays the correlations between all study variables for the two groups. Among high-BAS participants, M-SRM Regularity scores were negatively correlated with initial BDI scores, such that higher initial depressive symptoms were associated with lower social rhythm regularity. The only other significant correlation was between participants’ ages and ASRM scores, such that greater age was associated with lower initial hypomanic symptoms. For moderate-BAS participants, high baseline ASRM scores were associated with greater social rhythm regularity and greater likelihood of developing a BSD.
Correlations Among Study Variables
Table 3 presents the results of the hierarchical logistic regression analyses testing whether M-SRM Regularity at baseline predicted the likelihood of first onset of BSD among high-BAS and moderate-BAS participants, controlling for time in study, age, gender, baseline depressive and hypomanic symptoms, and family history of bipolar disorder. As shown in Table 3, none of the covariates entered in Step 1 significantly predicted BSD onset among high-BAS participants. However, consistent with this study’s hypothesis, when entered in Step 2, baseline M-SRM Regularity scores did significantly predict first onset of BSD, controlling for all covariates. Among high-BAS adolescents, those with lower Regularity scores were more likely to have a BSD onset than those with greater regularity. In contrast, among moderate-BAS participants, M-SRM Regularity scores did not predict onset of BSD, although greater initial hypomanic symptom severity on the ASRM did predict onset of BSD among moderate-BAS participants.
Hierarchical Logistic Regressions Predicting Likelihood of Bipolar Spectrum Disorder Onset
Potential Confounds
To be certain that the predictive association between lower social rhythm regularity and onset of BSD among high-BAS participants was not attributable to adolescents with the most irregular social rhythms having the highest BAS scores, we repeated the logistic regression analysis for the high-BAS group including BAS-Total scores as an additional, seventh covariate. This analysis revealed that M-SRM Regularity continued to predict first onset of BSD even while also controlling for BAS-Total scores (β = −.139, SΕ β = .072, Wald = 3.786, odds ratio [OR] = 0.870, p = .05).
Given the association between substance use disorders and high BAS/reward sensitivity (e.g., Alloy et al., 2009; Dawe & Loxton, 2004), as well as the possibility that extensive substance use may interfere with social rhythm regularity, we also controlled for a history of alcohol and drug use disorders as a possible confounding factor in the relationship between low social rhythm regularity and prospective first onset of BSD among high-BAS adolescents. Controlling for DSM–IV–TR or RDC alcohol or drug use disorders as an additional covariate, social rhythm regularity still predicted first onset of BSD among high-BAS participants (β = −.150, SE β = .072, Wald = 4.354, OR = .861, p = .037).
Exploratory Analysis
Inasmuch as social rhythm regularity includes stability of the sleep/wake cycle as one of its several components, we explored whether the Sleep/Wake Regularity score derived from the M-SRM would predict onset of BSD among high-BAS participants in a manner comparable to, or better than, the overall Regularity score. When we substituted Sleep/Wake Regularity for the overall Regularity scores in the analysis shown in Table 3 for high-BAS participants, sleep/wake regularity predicted onset of BSD at trend level significance (β = −.743, SE β = .461, Wald = 2.603, OR = .475, p = .107). Thus, sleep/wake regularity did not predict BSD onset as well as overall social rhythm regularity.
DiscussionThis study provided the first test of irregularity of social rhythms as a predictor of first onset of BSD. According to the social zeitgeber model of BSDs (Alloy et al., 2015; Ehlers et al., 1988; Grandin et al., 2006), changes in daily social rhythms or schedules lead to disruption of circadian rhythms and, in turn, onset of bipolar mood episodes. From this perspective, individuals with low regularity of social rhythms are hypothesized to be vulnerable to circadian rhythm disruption, and thus to bipolar episode onset. Given evidence for bidirectional influences between reward sensitivity and motivation and circadian rhythms (e.g., see Alloy et al., 2015, for review), and prior findings that reward hypersensitivity predicts first lifetime onset of BSD (Alloy et al., 2012), we examined the vulnerability status of low social rhythm regularity in adolescents with high reward sensitivity compared with those with moderate reward sensitivity.
Consistent with the social rhythm irregularity vulnerability hypothesis, we found that low social rhythm regularity significantly predicted a greater likelihood of first lifetime onset of a BSD among adolescents already at risk based on exhibiting reward hypersensitivity, but not among those with moderate reward sensitivity, controlling for length of follow-up, gender, age, baseline hypomanic and depressive symptoms, and family history of bipolar disorder. This was a very conservative test of the social rhythm regularity vulnerability hypothesis because it involved a truly prospective design in adolescents with no prior history of BSD and controlled for any initial subsyndromal depressive and hypomanic symptoms and family history of bipolar disorder. The predictive effect was maintained even when baseline reward sensitivity (BAS-T) scores and presence of alcohol and drug use disorders were controlled as additional covariates. It also was a conservative test because we used a baseline measure of social rhythm regularity as the predictor of first onset of BSD. Although the M-SRM was meant to assess more trait-like social rhythm regularity, it is impressive that this measure predicted BSD onset up to several months later. Potentially, even stronger prediction of BSD onset might be attained with repeated assessments of social rhythm regularity. Thus, this study provides the strongest evidence to date that low social rhythm regularity is a vulnerability factor for BSD, specifically among individuals with high reward sensitivity, and adds additional support for the social zeitgeber theory of BSDs.
Our results extend prior work demonstrating that low social rhythm regularity is associated with BSDs (Ashman et al., 1999; Jones et al., 2005; Shen et al., 2008; Szuba et al., 1992), as well as prior studies demonstrating that low social rhythm regularity predicts recurrences of bipolar mood episodes in individuals with BSDs (Shen et al., 2008), and that life events that disrupt social rhythms precipitate bipolar mood episodes (Malkoff-Schwartz et al., 1998, 2000; Sylvia et al., 2009). They also are consistent with prior findings that individuals at behavioral risk for BSDs exhibit low social rhythm regularity (Bullock et al., 2011; Meyer & Maier, 2006). Finally, along with Boland et al. (in press), these findings provide further evidence for a potential integration of reward hypersensitivity and social and circadian rhythm models of BSD (e.g., Alloy et al., 2015; Murray et al., 2009). In the Project TEAM sample, Boland et al. (in press) reported that high-BAS individuals were more likely than Moderate BAS individuals to experience social rhythm disruption in response to the occurrence of BAS-relevant life events, and that this social rhythm disruption mediated the association between the occurrence of BAS-relevant events and prospective hypomanic and depressive symptoms. The present findings build upon the Boland et al. study by demonstrating that more trait-like patterns of low social rhythm regularity also predict first onset of diagnosable BSD among individuals with reward hypersensitivity.
Our findings have potentially important clinical implications. They suggest that it may be possible to screen adolescents and identify those at risk for developing a BSD based on exhibiting both high reward sensitivity and low social rhythm regularity before onset occurs. As such, it also may be possible to develop early interventions for these at-risk adolescents targeted at either reward processing or regularizing daily schedules (Frank et al., 1997, 2005; Nusslock et al., 2009). Early identification of risk for BSDs is key to promoting more positive outcomes.
A major strength of this study is the truly prospective design with a theory-based assessment of social rhythm regularity. In addition, our study included a large, ethnically diverse community sample of adolescents, which should increase generalizability of study findings. We also used standardized diagnostic interviews, interviewers who were blinded to social rhythm regularity and other predictors, frequent assessment intervals allowing for sensitivity in assessing mood episodes, and highly conservative tests of the study hypothesis including controls for initial symptom levels, family history of bipolar disorder, and substance use disorders.
However, the study’s limitations need to be noted as well. First, although our sample was ethnically diverse and representative of the larger adolescent community population from which it was drawn on demographics, our findings may not generalize to other community samples or to clinical samples of adolescents. Second, our assessment of social rhythm regularity was based on a self-report measure only, albeit the reliable and valid SRM. Future studies would benefit from testing alternative measures of social rhythm regularity as vulnerabilities for onset of BSD. Third, although the M-SRM was designed to measure trait regularity of social rhythms, participants only completed it once at baseline. It is possible that the regularity of some individuals’ social rhythms changed over follow-up, and thus readministering the M-SRM at a later time point would have allowed us to confirm that participants maintained their regularity patterns over time. Fourth, family history of bipolar disorder was measured with the family history method rather than with more accurate direct interviews with relatives. Fifth, social rhythm regularity as assessed by the SRM likely involves several components, including engaging in activities that provide structure in one’s life and regularity of the sleep/wake cycle, to name two. It is not clear conceptually which of these two aspects of social rhythm regularity is central to its predictive association with BSD. Although we explored whether sleep/wake regularity predicted BSD onset as well as overall regularity did, and found that it did not, future research is needed to carefully conceptualize and parse apart the key active components of social rhythm regularity. Given the important role of sleep disturbance in circadian rhythm regularity and bipolar disorders (e.g., Murray & Harvey, 2010), it is likely that other approaches to assessing regularity of the sleep/wake cycle (e.g., actigraphy, dim light melatonin onset) may yield stronger predictive associations. Sixth, given low rates of BSD onset in our sample and the consequent limited statistical power, we could not fully test the specificity of the social rhythm regularity effect to high-BAS participants by examining the BAS Risk Group × Social Rhythm Regularity interaction in a full logistic regression model with all covariates. Finally, this study examined self-reported social rhythm regularity as a risk factor for BSD onset, but not desynchronization or disruption of circadian rhythms itself, which is hypothesized to be the more proximal mechanism underlying vulnerability to BSDs. Future studies should investigate circadian rhythm disruption itself as a predictor of first onset of BSD, as well as whether circadian rhythm disruption mediates the predictive association between social rhythm regularity and BSD onset.
ConclusionIn summary, this is the first prospective test of social rhythm regularity as a predictor of first lifetime onset of disorders in the bipolar spectrum. Keeping in mind the study limitations, our findings support the vulnerability hypothesis of the social zeitgeber model and indicate that irregularity of social rhythms is a vulnerability for first onset of BSDs among adolescents already at risk based on reward hypersensitivity.
Footnotes 1 The low base rate of first onsets of BSDs in the current sample, particularly in the moderate-BAS group, contributes to relatively low statistical power. As a result, it is difficult to demonstrate a significant interaction between BAS risk status and social rhythm regularity in predicting first onsets of BSDs with six or more needed covariates using a maximum likelihood procedure like logistic regression. When the BAS × Regularity interaction is added on the final step of the logistic regression controlling for the main effects of BAS risk and social rhythm regularity in the full sample, the interaction does not predict first onset of BSD significantly (OR = .98, ps = .23 to .28, depending on the number of covariates included). On the other hand, the social zeitgeber theory does not require that low social rhythm regularity serve as a risk factor for BSD, specifically among individuals with heightened reward sensitivity. However, given the bidirectional influence of the reward and circadian systems on each other, reward hypersensitivity may well potentiate a general tendency for social rhythm irregularity to contribute to BSD onset. Thus, low social rhythm regularity may predict BSD onset to a greater extent in high-BAS than moderate-BAS participants, as the results in Table 3 suggest.
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Submitted: January 23, 2015 Revised: August 9, 2015 Accepted: August 12, 2015
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Source: Journal of Abnormal Psychology. Vol. 124. (4), Nov, 2015 pp. 944-952)
Accession Number: 2015-52362-009
Digital Object Identifier: 10.1037/abn0000107
Record: 29- Title:
- Mandated college students’ response to sequentially administered alcohol interventions in a randomized clinical trial using stepped care.
- Authors:
- Borsari, Brian. Mental Health and Behavioral Sciences Service, Department of Veterans Affairs Medical Center, Providence, RI, US, Brian.Borsari@va.gov
Magill, Molly. Center for Alcohol and Addiction Studies, Brown University School of Public Health, RI, US
Mastroleo, Nadine R.. College of Community and Public Affairs, Binghamton University, NY, US
Hustad, John T. P.. Department of Medicine and Public Health Sciences, Pennsylvania State College of Medicine, PA, US
Tevyaw, Tracy O'Leary. Mental Health and Behavioral Sciences Service, Department of Veterans Affairs Medical Center, Providence, RI, US
Barnett, Nancy P.. Center for Alcohol and Addiction Studies, Brown University School of Public Health, RI, US
Kahler, Christopher W.. Center for Alcohol and Addiction Studies, Brown University School of Public Health, RI, US
Eaton, Erica. Center for Alcohol and Addiction Studies, Brown University School of Public Health, RI, US
Monti, Peter M.. Center for Alcohol and Addiction Studies, Brown University School of Public Health, RI, US - Address:
- Borsari, Brian, San Francisco VA Medical Center, (116B) 4150 Clement Street, San Francisco, CA, US, 94121, Brian.Borsari@va.gov
- Source:
- Journal of Consulting and Clinical Psychology, Vol 84(2), Feb, 2016. pp. 103-112.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 10
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- alcohol, peers, brief advice, brief motivational intervention
- Abstract (English):
- Objective: Students referred to school administration for alcohol policies violations currently receive a wide variety of interventions. This study examined predictors of response to 2 interventions delivered to mandated college students (N = 598) using a stepped care approach incorporating a peer-delivered 15-min brief advice (BA) session (Step 1) and a 60- to 90-min brief motivational intervention (BMI) delivered by trained interventionists (Step 2). Method: Analyses were completed in 2 stages. First, 3 types of variables (screening variables, alcohol-related cognitions, mandated student profile) were examined in a logistic regression model as putative predictors of lower risk drinking (defined as 3 or fewer heavy episodic drinking [HED] episodes and/or 4 or fewer alcohol-related consequences in the past month) 6 weeks following the BA session. Second, we used generalized estimating equations to examine putative moderators of BMI effects on HED and peak blood alcohol content compared with assessment only (AO) control over the 3-, 6-, and 9-month follow-ups. Results: Participants reporting lower scores on the Alcohol Use Disorders Identification Test, more benefits to changing alcohol use, and those who fit the 'Bad Incident' profile at baseline were more likely to report lower risk drinking 6 weeks after the BA session. Moderation analyses revealed that Bad Incident students who received the BMI reported more HED at 9-month follow-up than those who received AO. Conclusion: Current alcohol use as well as personal reaction to the referral event may have clinical utility in identifying which mandated students benefit from treatments of varying content and intensity. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Impact Statement:
- What is the public health significance of this article?—This study indicates that for mandated college students, the personal reaction to the referral event may have clinical utility in identifying which individuals benefit from treatments of varying content and intensity. In the context of stepped care, the findings provide support for the sequential delivery of 2 efficacious yet relatively low-intensity approaches that can be widely implemented with this at-risk population. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Alcohol Drinking Patterns; *College Students; *Intervention; *Motivational Interviewing; *Peers
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Alcohol Drinking in College; Alcohol-Related Disorders; Female; Humans; Male; Mandatory Programs; Outcome Assessment (Health Care); Peer Group; Psychotherapy, Brief; Young Adult
- PsycINFO Classification:
- Drug & Alcohol Rehabilitation (3383)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Alcohol and Drug Use Measure
Reasons for Limited Drinking Scale
Alcohol Use Disorders Identification Test DOI: 10.1037/t01528-000
Brief Sensation-Seeking Scale-4 DOI: 10.1037/t17549-000
Alcohol and Drug Consequences Questionnaire DOI: 10.1037/t04155-000
Young Adult Alcohol Consequences Questionnaire DOI: 10.1037/t03947-000
Brief Comprehensive Effects of Alcohol Scale DOI: 10.1037/t05132-000 - Grant Sponsorship:
- Sponsor: National Institute on Alcohol Abuse and Alcoholism, US
Grant Number: R01-AA015518, R01-AA017874
Recipients: Borsari, Brian
Sponsor: National Institute on Alcohol Abuse and Alcoholism, US
Grant Number: K23 AA018126
Recipients: Magill, Molly
Sponsor: National Institute on Alcohol Abuse and Alcoholism, US
Grant Number: T32 AA07459.
Recipients: Mastroleo, Nadine R.
Sponsor: National Center for Research Resources, US
Recipients: Hustad, John T. P.
Sponsor: National Institutes of Health, National Center for Advancing Translational Sciences, US
Grant Number: UL1RR033184, KL2RR033180
Recipients: Hustad, John T. P. - Methodology:
- Clinical Trial; Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Oct 12, 2015; Accepted: Aug 19, 2015; Revised: Jun 9, 2015; First Submitted: Oct 9, 2013
- Release Date:
- 20151012
- Correction Date:
- 20170306
- Copyright:
- American Psychological Association. 2015
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0039800
- PMID:
- 26460571
- Accession Number:
- 2015-46455-001
- Number of Citations in Source:
- 79
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-46455-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-46455-001&site=ehost-live">Mandated college students’ response to sequentially administered alcohol interventions in a randomized clinical trial using stepped care.</A>
- Database:
- PsycINFO
Mandated College Students’ Response to Sequentially Administered Alcohol Interventions in a Randomized Clinical Trial Using Stepped Care
By: Brian Borsari
Mental Health and Behavioral Sciences Service, Department of Veterans Affairs Medical Center, Providence, Rhode Island, and Center for Alcohol and Addiction Studies, Brown University School of Public Health;
Molly Magill
Center for Alcohol and Addiction Studies, Brown University School of Public Health
Nadine R. Mastroleo
College of Community and Public Affairs, Binghamton University
John T. P. Hustad
Department of Medicine and Public Health Sciences, Pennsylvania State College of Medicine
Tracy O’Leary Tevyaw
Mental Health and Behavioral Sciences Service, Department of Veterans Affairs Medical Center, and Center for Alcohol and Addiction Studies, Brown University School of Public Health
Nancy P. Barnett
Center for Alcohol and Addiction Studies, Brown University School of Public Health
Christopher W. Kahler
Center for Alcohol and Addiction Studies, Brown University School of Public Health
Erica Eaton
Center for Alcohol and Addiction Studies, Brown University School of Public Health
Peter M. Monti
Center for Alcohol and Addiction Studies, Brown University School of Public Health
Acknowledgement: Brian Borsari is now with the Department of Veteran’s Affairs Medical Center, San Francisco, California and the University of California-San Francisco.
Brian Borsari’s contribution to this article was supported by National Institute on Alcohol Abuse and Alcoholism (NIAAA) Grants R01-AA015518 and R01-AA017874. Molly Magill’s contribution was supported by NIAAA Grant K23 AA018126. Nadine Mastroleo’s contribution to this article was supported by NIAAA Grant T32 AA07459. John Hustad’s contribution was supported by the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant UL1RR033184 and KL2RR033180. The authors wish to thank Donna Darmody, John J. King, and Kathleen McMahon for their support of the project, as well as recognize all of the interventionists’ efforts. Colleen Peterson was also invaluable in data management and other technical aspects of this work. The contents of this article do not represent the views of the National Institute on Alcohol Abuse and Alcoholism, the Department of Veterans Affairs, or the United States Government.
Thousands of college students receive campus alcohol violations and mandatory alcohol interventions each year (Bernat, Lenk, Nelson, Winters, & Toomey, 2014). However, the mandated student population is a heterogeneous one, for which a “one size fits all” intervention approach may not be appropriate (Borsari, 2012; Merrill, Carey, Lust, Kalichman, & Carey, 2014). A stepped care approach may be an efficient way to address the needs of this population, as it provides a lower dose or minimal intervention initially and a more intensive intervention for those who do not respond to the lower dose intervention (Sobell & Sobell, 2000). Two promising candidates for inclusion in stepped care approaches for reducing alcohol use and consequences in college students are brief advice (BA; Fleming et al., 2010; Helmkamp et al., 2003; Kulesza, Apperson, Larimer, & Copeland, 2010; Schaus, Sole, McCoy, Mullett, & O’Brien, 2009) and brief motivational intervention (BMI; Carey, Scott-Sheldon, Carey, & DeMartini, 2007; Fachini, Aliane, Martinez, & Furtado, 2012; Larimer & Cronce, 2007). Additionally, research suggests the use of peer counselors (fellow undergraduate students who deliver evidence-based interventions) may be one way to enhance and reduce the costs of intervention approaches (Larimer et al., 2001; Mastroleo, Mallett, Ray, & Turrisi, 2008). Thus, the combination of a stepped care approach with a peer counselor delivery component has the potential to meet a number of individual student needs.
A stepped care trial conducted with mandated students (Borsari et al., 2012) first provided a BA session delivered by a peer counselor; 6 weeks later, students who exhibited risky drinking (defined as four or more heavy episodic drinking [HED] episodes and/or five or more alcohol-related consequences) were randomized to a second intervention (BMI) or assessment only (AO). At 9-month follow-up, students who had received a BMI significantly reduced the number of alcohol-related problems compared with students in the AO group. However, neither the BMI nor AO participants demonstrated reductions in alcohol use (HED, peak blood alcohol content [pBAC]). These findings suggest that implementing stepped care with mandated students can be effective in reducing alcohol-related harms. That said, it is important to efficiently and empirically identify individuals who will require more intensive intervention following a peer-led BA, as well as the characteristics of students who are more or less responsive to a professionally led BMI. This enhanced knowledge is vital for the efficient allocation of peer and professional intervention efforts and resources on campus.
Although predictors of response to sequentially administered BA and BMI have yet to be examined, predictors of response to BA and moderators of response to BMI administered in stand-alone interventions have emerged in the literature. For example, one trial found BA and BMI equally efficacious in reducing alcohol use but not problems, and these outcomes were mediated by both descriptive norms and coping skills (Kulesza, McVay, Larimer, & Copeland, 2013). Research with mandated students found that women reduced drinking more following a BMI than a computer-delivered intervention, whereas men reduced alcohol use following either intervention (Carey, Henson, Carey, & Maisto, 2009). Students with higher numbers of alcohol-related problems at baseline were more responsive to a BMI with written feedback than to written feedback alone (Mun, White, & Morgan, 2009). Characteristics of the referral incident have also been found to predict outcomes. Specifically, BMI is more effective than written feedback alone for students who experienced severe incidents requiring police or medical attention (Mun et al., 2009), but less effective than computer-delivered interventions for individuals who did not view the referral incident as aversive (Mastroleo, Murphy, Colby, Monti, & Barnett, 2011).
To our knowledge, no study has examined predictors of drinking response to two interventions delivered sequentially with mandated college students using a stepped care approach. There are several commonly assessed variables that have been associated with heavy alcohol use in college students. We examined three different types of variables. First, we wanted to examine whether four commonly obtained screening variables would inform response to the BA or BMI. Specifically, current risky drinking was of interest, as there has been mixed prior results as to whether heavier drinkers are more responsive or not to a BMI (Mun et al., 2009; Murphy et al., 2001). As previously discussed, gender has been associated with differential response to BMIs. As early onset of alcohol use has been consistently related to alcohol use and abuse in adults (DeWit, Adlaf, Offord, & Ogborne, 2000; Grant & Dawson, 1997; Morean, Corbin, & Fromme, 2012), students with an earlier age of first drink (AFD) may be less likely to reduce their drinking following a BA and/or BMI intervention. Sensation seeking (SS) is a personality trait with a biological basis that expresses itself as a need for physiological arousal (Quinn, Stappenbeck, & Fromme, 2011; Stephenson, Hoyle, Palmgreen, & Slater, 2003), and has been linked directly (Woicik, Stewart, Pihl, & Conrod, 2009, Studies 1 and 2) and indirectly to alcohol use in mandated (Pearson & Hustad, 2014) and incoming (Hustad, Pearson, Neighbors, & Borsari, 2014) college students.
Alcohol-related cognitions are also commonly assessed in mandated college students. Perhaps the most commonly assessed cognitions are alcohol-related expectancies and descriptive norms. Alcohol-related expectancies are beliefs about the cognitive, affective, or behavioral effects of alcohol use, and can be both positive (e.g., “Drinking allows me to relax around others”) and negative (e.g., “When I drink, I often say things that I regret later”). However, recent reviews reveal inconsistent findings regarding expectancy interventions and drinking and alcohol-related outcomes (Cronce & Larimer, 2011; Labbe & Maisto, 2011; Scott-Sheldon, Terry, Carey, Garey, & Carey, 2012). Descriptive norms refer to the perception of other’s quantity and frequency of drinking, and are based largely on observations of how people consume alcohol in discrete drinking situations (Perkins, 2003). Descriptive norms have been linked to college alcohol use (see Borsari & Carey, 2001, 2003) and have been used commonly in interventions addressing college student drinking (Lewis & Neighbors, 2006; M. B. Miller et al., 2013). We were also particularly interested in two other cognitions related to alcohol use. Reasons for limited drinking (RFLDs; Greenfield, Guydish, & Temple, 1989) in college students have been found to be negatively related to binge drinking and alcohol-related consequences (Collins, Koutsky, Morsheimer, & MacLean, 2001; Epler, Sher, & Piasecki, 2009; Palfai & Ralston, 2011), and positively related to interest in receiving alcohol-focused treatment (Epler, Sher, Loomis, & O’Malley, 2009). Finally, the participants’ perceptions of the costs and benefits of change may make mandated students more receptive to intervention. This may be especially true regarding a BMI that incorporates motivational interviewing (MI; W. R. Miller & Rollnick, 2012), a style “designed to strengthen personal motivation and commitment to a specific goal by eliciting and exploring the person’s own reasons for change” (p. 29).
It is also possible that a combination of factors, rather than a single variable, is associated with lower risk drinking in response to BA and/or BMI. Work from our research group (Barnett et al., 2008) derived three distinct profiles of mandated students based on their typical rates of alcohol use and problems, drinking on the day of the referral event, and personal responsibility and aversiveness of the incident. These profiles were (a) “Bad Incident,” which was characterized by low scores for heavy drinking and problems, and high levels of referral event drinking, responsibility, and aversiveness; (b) “So What?,” which was characterized by high heavy drinking and alcohol-related problems, moderate incident drinking and responsibility, and low aversiveness; and (c) “Why Me?,” which was characterized by a pattern of relatively low heavy drinking and alcohol-related problems, very little incident drinking, and low responsibility and aversiveness. Perhaps such a multifaceted and empirically derived mandated student profile may be the most informative way to predict response to a BA and/or BMI.
Current StudyThe purpose of this study was to evaluate whether a peer-delivered BA or professionally delivered BMI was more or less effective with certain individuals. To do so, we examined differential response to BA and BMI in a clinical trial with high fidelity to the two interventions, low attrition rates, and high participant satisfaction (Borsari et al., 2012). Two sets of analyses were conducted to examine the utility of three different types of variables in predicting response to two interventions (BA and BMI). First, we first examined which screening variables (risky drinking, gender, age of first drink, sensation seeking), alcohol-related cognitions (alcohol expectancies, perceived descriptive norms, reasons for limited drinking, costs and benefits of change), and mandated student profile (Bad Incident, So What?, and Why Me?) predicted lower risk drinking following a BA session. In the context of the stepped care trial, lower risk drinking was defined as three or fewer heavy drinking episodes and four or fewer alcohol-related consequences in the past month. Second, we conducted moderation analyses to identify the certain conditions under which BMI may be more effective than AO in reducing alcohol use over 3-, 6-, and 9-month follow-ups. The nonsignificant differential effects across the BMI and AO conditions in alcohol use provide a compelling rationale for moderation analyses incorporating a subset of the screening variables, alcohol-related cognitions, and mandated student profile. Placed in the larger context, this study will improve our understanding of which students may be more responsive to two commonly delivered interventions of different length and complexity.
Method Design
This study implemented stepped care with undergraduate students age 18 years and older who violated campus alcohol policy at a 4-year college in the Northeast United States (Borsari et al., 2012). There were two steps of intervention. All participants received Step 1, a peer-delivered BA session. Mandated students who reported continued risky alcohol use 6 weeks after the BA session were randomized by computer to (a) Step 2, a 60- to 90-min BMI, or (b) AO control. Participants then completed 3-, 6-, and 9-month follow-ups via Internet (see Figure 1).
Figure 1. Randomized clinical trial implementing stepped care for mandated college students. A = Assessment 6 weeks following BA session; BMI = brief motivational intervention; R = randomization; Lower Risk = three or fewer heavy episodic drinking (HED) episodes and/or four or fewer problems endorsed on the Young Adult Alcohol Consequences Questionnaire (YAACQ) in the past month; Higher Risk = four or more HED episodes and five or more problems endorsed on the YAACQ in the past month.
Participants
Prospective participants (N = 982) had been referred to the student health office (SHO) for mandatory counseling following adjudication by campus judicial affairs staff. Of these students, 598 (61%) agreed to participate in the research study and provided informed consent. Students who declined to participate (n = 384) in the project received treatment as usual from the SHO, consisting of a 15- to 30-min individual discussion of their referral incident and alcohol use. The majority of those who refused to participate (70%) cited time constraints as their reason (the baseline assessment and intervention took 20 to 30 min longer than treatment as usual). Participants received $15 for the baseline assessment, $40 for the 6-week assessment, and $25, $35, and $60 for the 3-, 6-, and 9-month assessments, respectively. The university institutional review board of the study site approved all procedures.
Interventions
Step 1: BA
The manualized BA was administered by a trained SHO peer (i.e., fellow undergraduate college student) counselor and lasted approximately 15 min. The peer counselor facilitated discussion of the events leading to the referral incident, the reactions of friends and family, and any changes the student had made to his or her drinking as a result. The peer counselor also provided a 12-page booklet containing educational information (from Cunningham, Wild, Bondy, & Lin, 2001). The BA session was mostly didactic, but the peer counselors did solicit personal information from participants often, using open-ended questions. Throughout the session, participants were given the opportunity to ask questions or discuss their personal alcohol use with the peer counselor.
Assignment to Step 2
Consistent with a stepped care strategy, the tailoring variables in this project were HED episodes and alcohol-related problems. The decision rule was that higher risk students (reporting four or more HED episodes and/or five or more alcohol-related consequences in the past month) were randomized to receive AO or Step 2 (BMI). Participants reporting lower risk drinking (three or fewer HED episodes and/or four or fewer alcohol-related consequences in the past month) were provided no additional intervention.
Step 2: BMI
Adapted from previous interventions with college students (Dimeff, Baer, Kivlahan, & Marlatt, 1999), this manualized BMI has resulted in significant reductions in alcohol use and problems with mandated and nonmandated students in other trials (Borsari & Carey, 2000, 2005; Carey et al., 2009; Hustad, Mastroleo, et al., 2014). The BMIs were delivered by 11 interventionists who were doctoral-level students or postdoctoral fellows. At the beginning of the BMI, the participant was given a personalized feedback report of his or her responses to the baseline and 6-week follow-up, including normative quantity/frequency of drinking, BAC and tolerance, alcohol-related consequences, influence of setting on drinking, and alcohol expectancies. Throughout the BMI, which lasted approximately 45 to 60 min, interventionists followed the four principles of MI: express empathy, develop discrepancy, roll with resistance, and support self-efficacy for change (see W. R. Miller & Rollnick, 2002).
Measures
Alcohol use
Both tailoring variables for the decision rules and outcome variables were obtained using the Alcohol and Drug Use Measure (Borsari & Carey, 2000, 2005). The tailoring variable for response to the BA session was number of HED episodes obtained using a gender-specific question that asked participants to report the number of times they consumed five or more drinks for males (four or more for females) in the past month. The maximum number of drinks consumed during the past month and the amount of time spent drinking during this episode to calculate the students’ estimated pBAC, using the Matthews and Miller (1979) equation and an average metabolism rate of 0.017g/dL per hour. pBAC and HED were used as outcome variables in the moderation analyses.
Alcohol-related consequences
Alcohol-related consequences were used as a tailoring variable and were assessed by the 48-item Young Adult Alcohol Consequences Questionnaire (YAACQ; Read, Kahler, Strong, & Colder, 2006). Dichotomous items (yes–no) were summed for a total number of alcohol-related consequences experienced in the past month. The YAACQ demonstrated high internal consistency in this sample (α = .94 at 6-week assessment).
Screening variables
Risky drinking at baseline was assessed by the Alcohol Use Disorders Identification Test (AUDIT; Saunders, Aasland, Babor, de la Fuente, & Grant, 1993). The AUDIT is a 10-item questionnaire that assesses quantity and frequency of alcohol use as well as concerns of others regarding one’s drinking, and scores on the AUDIT can range from 0 to 40 (higher scores indicate riskier drinking). The AUDIT is commonly used as a screening tool with mandated college students (e.g., DeMartini & Carey, 2012) and exhibited good internal consistency with this sample (α = .75). We also assessed gender and recorded age of first drink by asking the student when he or she first started drinking, not counting small tastes or sips of alcohol (e.g., Grant & Dawson, 1997). Sensation seeking was measured by the Brief Sensation Seeking Scale-4 (BSSS-4; Stephenson et al., 2003), a four-item true–false measure derived from the 80-item BSSS (Hoyle, Stephenson, Palmgreen, Lorch, & Donohew, 2002), which determines the extent to which the participant engages in or would like to engage in activities that provide novel or intense sensations or experiences. The BSSS-4 demonstrated good internal consistency in this study at baseline (α = .80) and 6-week assessment (α = .79).
Alcohol-related cognitions
Alcohol expectancies were assessed using the Brief Comprehensive Effects of Alcohol Scale (Ham, Stewart, Norton, & Hope, 2005), a 15-item measure assesses valuations (i.e., the extent to which a student believes a certain effect to be “good” or “bad”) of both positive (e.g., “I would act sociable”) and negative (e.g., “I would feel dizzy”) alcohol expectancies. Students reported valuations of these expectancy outcomes using a 5-point scale (1 = bad to 5 = good) and computed mean positive (α = .82) and negative (α = .83) valuations for each participant. This measure has also been used with mandated students (Borsari, O’Leary Tevyaw, Barnett, Kahler, & Monti, 2007). Descriptive norms were assessed using the Drinking Norms Rating Form (Baer, Stacy, & Larimer, 1991), a three-item measure recorded the participants’ estimates of their own weekly alcohol consumption, as well as that of close friends and the typical student at the college. In this study, we created difference scores of personal alcohol consumption minus perceived norms for close friend and the typical student. The Reasons for Limited Drinking Scale (Greenfield et al., 1989) is a 19-item measure that assesses four reasons for regulating alcohol use: self-control (e.g., concern about alcohol-related problems), upbringing (e.g., religion discourages drinking), self-reform (e.g., seeing negative consequences in others), and performance (e.g., cognitive or motor impairments). Participants answer the items on a 4-point scale ranging from 0 (not applicable) to 3 (very important). This measure demonstrated good reliability in this sample at baseline (α = .87) and 6-week assessment (α = 89). Finally, the students’ perceptions of the costs and benefits of change were measured using the Alcohol and Drug Consequences Questionnaire (Cunningham, Sobell, Gavin, Sobell, & Breslin, 1997), a 29-item measure that assesses the costs and benefits of changing personal alcohol use. There are two scales: Costs (14 items) and Benefits (15 items). Participants are asked to rate each item (e.g., “I will have difficulty relaxing”) on a scale ranging from 1 (not important) to (extremely important); items may also be determined as not applicable (0). Excellent internal consistency was evident for both costs (baseline, α = .92; 6-week, α = .93) and benefits (baseline, α = .91; 6-week, α = .93) scales. In this study, we used the difference (benefits minus costs) to represent a single measure of whether the participant saw more benefit (positive sum) or costs (negative sum) to reducing their alcohol use.
Mandated student profile
Using methods identical to previous work (Barnett et al., 2008), we used baseline data to conduct a cluster analysis (Johnson & Wichern, 1998) to classify mandated students on the basis of five standardized variables (number of HED episodes and the number of alcohol-related problems in the past month, number of drinks consumed on the day of the referral incident, and perceived responsibility and aversiveness of the referral incident). Differences among the profiles were examined using ANOVA and Scheffé pairwise comparisons, and approximated those developed in Barnett et al. (2008) in several ways. First, the Bad Incident (n = 116) profile reported the lowest past-month alcohol use and problems, moderate number of drinks during the incident, and highest incident responsibility and aversiveness. The Bad Incident profile also reported a large proportion of higher risk referrals, such as vandalism and being drunk in public (21%) as well as being medically evaluated for intoxication or sent to an emergency room (both 2%). Second, participants assigned to the So What? profile (n = 169) reported the highest past-month drinking and problems, highest drinks on the night of the incident, and highest level of responsibility for the incident, yet lower aversiveness. Most of the referrals for So What? students tended to be for alcohol possession (90%) and higher risk referrals such as vandalism and drunk in public (8%). Finally, the Why Me? (n = 242) profile reported low rates of past-month drinking and problems, lowest number of drinks on the night of the incident, and low levels of responsibility aversiveness. The majority of their offenses were possession (75%) or being in the presence (21%) of alcohol.
Data Analysis
For our first analysis, we conducted a multivariate logistic regression model predicting the odds of reporting lower risk drinking after the BA session (i.e., reporting fewer than three HED episodes and/or a score of 4 or less on the YAACQ). Putative predictors were screening variables (gender, AUDIT scores, age of first drink, sensation seeking), alcohol-related cognitions (descriptive norms, alcohol expectancies, reasons for limiting drinking, and costs and benefits of change), and the mandated student profile (Bad Incident, So What?, Why Me?). The proposed predictors, assessed at baseline, were additionally examined with means, standard deviations, and frequencies.
Our second analysis examined moderation of BMI’s effect on alcohol use (HED and pBAC). These moderation analyses were conducted only with the students who met risky drinking criteria at 6 weeks and who completed at least one 3-, 6- and 9-month follow-up (i.e., non-BA responders; approximate ns: BMI = 184, AO = 180), and incorporated the same variables as in our first analysis, with three exceptions. Specifically, we did not examine AUDIT scores, descriptive norms, and expectancies as moderators, as these were explicitly addressed in the context of the BMI and are therefore more appropriately conceptualized as mediators of BMI effects (Apodaca & Longabaugh, 2009; Borsari & Carey, 2000, 2005). Furthermore, moderator variables were measured at the 6-week follow-up (prior to the Step 2 BMI), with the exception of gender, age at first drink, and mandated student profile (which were assessed at baseline). Moderator analyses were conducted using generalized estimating equations (GEEs; Liang & Zeger, 1986) using models described by Aiken and West (1991) that included the independent and moderator variable effects as well as a multiplicative term for their interaction. If moderation effects were detected, BMI and AO effects were examined at specified time points (3, 6, and 9 months). GEE models covaried the Time 1 (6-week assessment occurring before the BMI session) value of the dependent variable, and were conducted using Gaussian distributional assumptions and an autoregressive (AR1) correlation structure. All analyses were conducted in SPSS version 20.0.
Results Sample
Participants were 67% male, 96% Caucasian, and 68% freshman with a mean age of 18.68 years (SD = 0.78). They were cited for possession of alcohol (78.18%), being in the presence of alcohol (12.14%), alcohol-related behavior (9.30%), and alcohol-related medical complications (0.38%). All 598 students who agreed to participate completed a 45-min baseline assessment immediately prior to receiving the Step 1 BA. Of those 598 students, 582 successfully completed the 6-week follow-up web assessment (95%), and 102 were designated low risk and 462 were designated high risk. Fifty-seven students were assigned to the BMI or AO during the summer months (see Borsari et al., 2014), and the remaining 405 participants were assigned to BMI (n = 211) or AO (n = 194).
Predictors of Lower Risk Drinking Following a BA Session
As shown in Table 1, participants with higher baseline AUDIT scores were less likely to report lower risk drinking (odds ratio [OR] = 0.831; 95% confidence interval [CI] [0.76, 0.91]) 6 weeks following the BA session, and participants reporting more benefits than costs to changing their alcohol use (OR = 1.022; 95% CI [1.006, 1.039]) were more likely to report lower risk drinking following a BA session. In addition, compared with the Bad Incident profile, mandated students assigned to the So What? and Why Me? profiles were less likely to report lower risk drinking following a BA session (OR = 0.188, 95% CI [0.050, 0.07], and OR = 0.251, 95% CI [0.12, 0.51], respectively). All remaining predictors were nonsignificant.
Logistic Regression Predicting Lower Risk Drinking 6 Weeks After Brief Advice Session
Moderators of BMI Effects
Moderator analyses showed that gender, age of first drink, sensation seeking, costs and benefits of change, and reasons for limited drinking did not significantly moderate the impact of BMI relative to AO over time on HED and pBAC (all ps > .05). However, GEE results showed a moderation effect for mandated student profile. Students that belonged to the Bad Incident profile had the greatest increase in HED when assigned to BMI (B = 1.63 [.77], p = .04). Figure 2 demonstrates the difference in HED between the BMI and AO groups by mandated student profile at 9-month follow-up. For covariate effects across all models, the 6-week value of the outcome variable was positively associated with greater alcohol use (Brange = .47 to 1.22; ps < .001).
Figure 2. Difference between BMI and AO groups on 9-month HED episodes by mandated student profile. BMI = brief motivational intervention; AO = assessment only; HED = heavy episodic drinking. See the online article for the color version of this figure.
DiscussionTo our knowledge, this study represents the first attempt to systematically identify which mandated students may respond best to sequentially administered BA and/or BMI. As has been posited previously (Barnett et al., 2008; Borsari & O’Leary Tevyaw, 2005), the lower risk drinking exhibited by approximately 20% of the participants 6 weeks after a BA session indicates that all mandated students do not require an intensive BMI to reduce their alcohol-related problems. Thus, these findings can assist school administrations in strategically allocating their clinical resources by careful consideration of which mandated students respond to BA and/or BMI.
Regarding response to the BA session, students with lower AUDIT scores and perceiving more benefits than costs to reducing their drinking were less likely to be eligible for a more intensive Step 2 BMI when they were assessed 6 weeks later. These findings intuitively indicate that BA may be particularly useful for those who are drinking less and already see the benefits of reducing alcohol use. Furthermore, the lack of descriptive norms and expectancies in predicting lower risk drinking following the BA session suggests that these constructs may be best conceptualized as mediators of intervention effects. This would be consistent with recent research showing that mandated students who had higher close-friend norms were less likely to reduce their drinking regardless of alcohol sanctions than students who perceived lower close-friend norms (Merrill, Carey, Reid, & Carey, 2014). Therefore, these alcohol cognitions may need to be explicitly addressed in the context of the intervention in order to be linked to outcome. Regarding moderation of BMI effects on alcohol use, none of the screening or alcohol-related cognitions significantly moderated BMI effects on HED or pBAC over the 9-month follow-up.
Perhaps the most compelling finding is the differential response of the Bad Incident profile to the BA and BMI. Namely, students in the Bad Incident group were more likely to respond to the BA session (38% reported lower risk drinking) than those in the Why Me? and So What? groups (6% and 4%, respectively). Considered in the context of research indicating that a significant subset of mandated students reduce their alcohol use on their own prior to intervention or that they require very little additional intervention (Carey et al., 2009; Hustad et al., 2011; Morgan, White, & Mun, 2008), these findings suggest that a peer-led BA session may be an appropriate intervention for a considerable number of mandated students who are light drinkers and/or have significantly reduced their use as a result of the referral incident. Indeed, there has also been increased understanding of what aspects of the referral incident can contribute to these reductions. For example, in a 6-month prospective study of over 2,200 college students, Wray, Simons, and Dvorak (2011) found that students who were light drinkers did not change their drinking following an infraction. The authors posited that as these students were already drinking lightly, the infraction did not result in a reconsideration and change of personal alcohol use. For the heavy drinkers, however, those most likely to reduce their alcohol use following an alcohol infraction reported higher sensitivity to punishment, indicating that the referral incident was viewed as aversive and led to subsequent changes in drinking behaviors. Likewise, Qi, Pearson, and Hustad (2014) found that high levels of incident aversiveness and personal responsibility were linked with higher readiness to change drinking following the incident, whereas Mastroleo et al. (2011) found that students who reported low levels of aversiveness to the referral incident reported higher levels of alcohol use following a booster session addressing their alcohol use. This research, combined with the findings of this study, suggest that a multifaceted profile can have clinical utility in identifying which mandated students could benefit from more intensive treatment.
The students in the Bad Incident group who received a BA, and then a more intensive BMI, demonstrated increased HED compared with those assigned to the assessment-only group at the 9-month follow-up. In contrast, there were no moderation effects evident in the other two profiles that were less likely to respond to the BA. This response by Bad Incident (and lighter drinkers) to a personalized BMI contradicts results from a recent integrated data analysis of 24 independent trials administering BMIs to over 6,000 college students, 18% of which were mandated students (Ray et al., 2014). Findings indicated that BMI efficacy at longer term follow-ups (6 to 12 months) is linked to an interaction between the number of topics addressed during a BMI and the degree to which the feedback was personalized to reflect the student’s own situation. Specifically, reductions in alcohol use are greater following BMIs that either (a) provide highly personalized information on a large number of topics, or (b) provide more generic information on a fewer number of topics. In this study, Bad Incident students increased their alcohol use following a professionally delivered BMI with a large number of highly personalized feedback topics (including comparison with national and campus norms, self-reported consequences at baseline as well as 6-week assessment).
We can only speculate why there were increases in HED in this subset of Bad Incident students. Although there were no significant changes in alcohol use over the 9-month follow-up by participants in the BMI and AO groups, supplemental analyses indicated that the Bad Incident students reported significantly lower rates of drinking and alcohol-related problems at the 6-week assessment than students in the other two profiles. Therefore, delivering a BMI to individuals who were slightly above the drinking cutoffs implemented in the stepped care design may have resulted in reactance to the intervention. Although examination of satisfaction ratings of the BMI revealed no significant differences among the three profiles, the personalized feedback in the BMI may have affirmed maintaining the status quo (i.e., not changing alcohol use) rather than facilitating motivation to change. In this context, and following a BA session, perhaps Bad Incident students may have found the personalized feedback as irrelevant or unconvincing—especially if significant reductions had occurred following the incident. Such a reaction to the BMI may be akin to the iatrogenic effects observed when conducting a decisional balance exercise with an individual already motivated to reduce drinking (W. R. Miller & Rose, 2015).
Regarding clinical implications of these findings, the variables that predicted lower risk drinking following the peer-led BA, or moderated the effects of a professionally delivered BMI, may be useful in identifying students that may be more receptive to the interventions provided. That said, one could argue that the significance of AUDIT scores proposes a simpler and more intuitive rule of thumb: provide the heavier drinkers with a BMI. Yet simply using alcohol consumption and related problems as a referral strategy might not be as clinically useful as other constructs. In addition, the moderators of treatment response can be a focus of the BMI sessions and perhaps be more relevant, and of intrinsic interest, to the student than focusing solely on drinking and consequences assessed by the AUDIT or other measures. Efforts to balance the personalization, as well as number and content, of feedback topics will be informed by previously mentioned work by Ray and colleagues (2014) and other research reporting that college students’ (especially heavier drinkers) least favorite feedback topics were personal drinking profile and didactic information about alcohol (M. B. Miller & Leffingwell, 2013).
This study should be considered in the context of some limitations. First, the cutoffs determining lower risk drinking were developed by the authors, limiting the generalizability of these findings to future applications of stepped care with mandated students using more (or less) stringent decision rules for assignment to more intensive interventions. The lack of response of the Bad Incident students to the more intensive BMI may have been an artifact of using solely alcohol use and problems as tailoring variables. Perhaps using an empirically derived and multifaceted profile may be more efficient (e.g., target those individuals reporting low averseness/responsibility for incident in addition to risky alcohol use). Second, the lack of a non-BA comparison group precludes our ability to state how much the BA contributed to the lower risk drinking observed, and it is possible that some participants who were below the cutoff at Step 2 had not been above it at baseline. Third, the sample was predominately White and was recruited from a small college in the Northeast. Therefore, findings may not generalize to schools with different demographic characteristics and/or campuses with different alcohol policies and enforcement strategies. Fourth, although there is little evidence of significant under- or overreporting of alcohol use in mandated students (Borsari & Muellerleile, 2009), self-report was not confirmed by collaterals.
Study findings also suggest promising directions for future research. First, variability in the direction of results by outcome (i.e., alcohol) underscores the complexity of evaluating the response to sequentially delivered interventions of different length and content, as required in stepped care. Therefore, a task for future studies will first be to gain greater clarity in determining who best responds to what intervention when, in mandated students as well as other populations. Second, an analysis of what actually is occurring in the BA and BMI sessions may be enlightening to understand the changes (or lack thereof) following intervention. Coding therapist and client language, and linking these interactions to subsequent behavior change, may be one way to better understand the mechanisms of change. Although there have been recent efforts to code BMIs with mandated college students (Apodaca et al., 2014; Mastroleo, Magill, Barnett, & Borsari, 2014), several research questions remain. For example, process coding the peer-delivered BA sessions would be compelling, as efficacy studies have shown that the same peer-delivered BMI utilized in this study significantly reduced alcohol consumption and negative consequences with volunteer (Larimer et al., 2001; Mastroleo, Turrisi, Carney, Ray, & Larimer, 2010) and mandated (Mastroleo et al., 2014) students. Although effective in reducing drinking with college students, the use of peers as intervention agents has gained surprisingly little attention in the literature. Furthermore, the manner in which peer counselors interact and are accepted by the students with whom they work is unknown. Fromme and Corbin (2004) reported that professional counselors consistently had better ratings of intervention delivery than peer counselors; however, the intervention was a multicomponent group intervention, not BMI. In the current study, a peer-led BA session did not lead to reductions in risky drinking for a considerable number of mandated students in the So What? and Why Me? profiles. Comparison of in-session processes by student profiles may reveal in-session differences and shed light on the observed effects, differences that were not captured by comparison of session satisfaction ratings or other self-report measures.
In summary, intervention outcomes vary according to individual characteristics. Placed in a larger context of the literature, a refinement of the stepped care decision rules is expected to enable practitioners and researchers to increase the efficiency and response following an intervention. Ideally, these results can further guide efforts to systematically combine these approaches in ways that enhance the efficiency of intervention timing, delivery, and content of intervention based on personal characteristics (e.g., AUDIT scores, perceived benefit to change alcohol use, Bad Incident profile, and HED frequency). In this way, the findings of the study inform the larger stepped care literature that seeks to develop a menu of efficacious yet relatively low-intensity approaches that can be widely implemented (e.g., McKellar, Austin, & Moos, 2012).
Footnotes 1 We developed a decision rule that incorporated both (a) HED, and (b) alcohol-related problems. Regarding HED, 44% of college students reported HED one or two times per week (Wechsler et al., 2002), a level we felt would be appropriate for Step 2 (BMI). Using a distribution of the YAACQ in a nonmandated sample (Kahler, Strong, & Read, 2005), we estimated that only about 25% of the sample would report five or more consequences in the past month on the YAACQ and therefore be appropriate for Step 2 (see Borsari et al., 2012, for more detail regarding the development of the decision rule).
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Submitted: October 9, 2013 Revised: June 9, 2015 Accepted: August 19, 2015
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Source: Journal of Consulting and Clinical Psychology. Vol. 84. (2), Feb, 2016 pp. 103-112)
Accession Number: 2015-46455-001
Digital Object Identifier: 10.1037/a0039800
Record: 30- Title:
- Mental disorders among undocumented Mexican immigrants in high-risk neighborhoods: Prevalence, comorbidity, and vulnerabilities.
- Authors:
- Garcini, Luz M.. Department of Psychology, Rice University, Houston, TX, US, lmg7@rice.edu
Peña, Juan M.. Department of Psychology, University of New Mexico, Albuquerque, NM, US
Galvan, Thania. Department of Psychology, University of Denver, Denver, CO, US
Fagundes, Christopher P.. Department of Psychology, Rice University, Houston, TX, US
Malcarne, Vanessa. Joint Doctoral Program in Clinical Psychology, San Diego State University, San Diego, CA, US
Klonoff, Elizabeth A.. Office of Graduate Studies, University of Central Florida, Orlando, FL, US - Address:
- Garcini, Luz M., Department of Psychology, Rice University, 6100 Main Street, Houston, TX, US, 77005, lmg7@rice.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 85(10), Oct, 2017. pp. 927-936.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 10
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- undocumented, Latinx, Mexican, mental illness, mental disorders
- Abstract (English):
- Objective: This study aimed to: (a) provide population-based estimates for the prevalence of mental disorders, including substance use, among undocumented Mexican immigrants; (b) assess for relevant comorbidities; and (c) identify sociodemographic, immigration and contextual vulnerabilities associated with meeting criteria for a disorder. Method: This cross-sectional study used Respondent Driven Sampling (RDS) to collect and analyze data from clinical interviews with 248 undocumented Mexican immigrants residing near the California–Mexico border. The M.I.N.I. Mini International Neuropsychiatric Interview was used as the primary outcome of interest. For all analyses, inferential statistics accounted for design effects and sample weights to produce weighted estimates. Logistic regression was used in multivariate analyses. Results: Overall, 23% of participants met criteria for a disorder (95% CI = 17.1; 29.0). The most prevalent disorders were Major Depressive Disorder (14%, 95% CI = 10.2; 18.6), Panic Disorder (8%, 95% CI = 5.0; 11.9) and Generalized Anxiety Disorder (7%, 95% CI = 3.4; 9.8). Approximately 4% of participants met criteria for a substance use disorder (95% CI = 1.2; 6.1). After controlling for covariates, being 18 to 25 years and experiencing distress from postmigration living difficulties were significantly associated with meeting criteria for a disorder. Conclusion: Undocumented Mexican immigrants are an at-risk population for mental disorders, particularly depression and anxiety disorders. Given that distress from postmigration living difficulties is associated with meeting criteria for a disorder, revisiting policies and developing new alternatives to facilitate access and provision of context-sensitive mental health services for this population is necessary to protect the human rights of these immigrants and that of their U.S. families. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Impact Statement:
- What is the public health significance of this article?—To our knowledge, this is the first study to provide population-based estimates for the prevalence of current mental and substance use disorders among undocumented Mexican immigrants residing in high-risk neighborhoods. This information is essential to inform advocacy efforts, break down existing stereotypes, and inform the development and provision of contextually and culturally sensitive mental health interventions for this at-risk immigrant population. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Comorbidity; *Immigration; *Mental Disorders; *Mexican Americans; Demographic Characteristics; Drug Abuse; Epidemiology; Generalized Anxiety Disorder; Major Depression; Panic Disorder; Posttraumatic Stress Disorder; Susceptibility (Disorders)
- PsycINFO Classification:
- Psychological Disorders (3210)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs) - Tests & Measures:
- San Diego Labor Trafficking Survey Questionnaire
Harvard Trauma Questionnaire-Adapted--Spanish Version
Structured Clinical Interview for DSM
Composite International Diagnostic Interview DOI: 10.1037/t02121-000
Mini International Neuropsychiatric Interview DOI: 10.1037/t18597-000 - Grant Sponsorship:
- Sponsor: Ford Fellowship
Recipients: Garcini, Luz M.
Sponsor: UC
Other Details: MEXUS Award
Recipients: Garcini, Luz M.
Sponsor: Sponsor name not included
Grant Number: 5R25GM058906-16
Other Details: Minority Biomedical Research Support Initiative for Maximizing Student Development
Recipients: Peña, Juan M.
Sponsor: Institute for Behavioral and Community Health, Training and Mentoring Program
Grant Number: 5R25MD006853-05
Recipients: Peña, Juan M. - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Jun 22, 2017; Revised: Jun 19, 2017; First Submitted: Mar 14, 2017
- Release Date:
- 20170928
- Copyright:
- American Psychological Association. 2017
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/ccp0000237
- Accession Number:
- 2017-42717-001
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2017-42717-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2017-42717-001&site=ehost-live">Mental disorders among undocumented Mexican immigrants in high-risk neighborhoods: Prevalence, comorbidity, and vulnerabilities.</A>
- Database:
- PsycINFO
Mental Disorders Among Undocumented Mexican Immigrants in High-Risk Neighborhoods: Prevalence, Comorbidity, and Vulnerabilities
By: Luz M. Garcini
Department of Psychology, Rice University;
Juan M. Peña
Department of Psychology, University of New Mexico
Thania Galvan
Department of Psychology, University of Denver
Christopher P. Fagundes
Department of Psychology, Rice University
Vanessa Malcarne
Joint Doctoral Program in Clinical Psychology, San Diego State University, and Joint Doctoral Program in Clinical Psychology, University of California San Diego
Elizabeth A. Klonoff
Office of Graduate Studies, University of Central Florida
Acknowledgement: Funding was provided to Luz Garcini by the Ford Fellowship and UC MEXUS Award and to Juan M. Peña by the Minority Biomedical Research Support Initiative for Maximizing Student Development (Grant 5R25GM058906-16) and the Training and Mentoring Program at the Institute for Behavioral and Community Health (Grant 5R25MD006853-05). In writing of this article, Juan M. Peña was also supported by the National Science Foundation Graduate Research Fellowship Program (Grant DGE-1418062).
A controversial issue at the forefront of the United States (U.S.) political agenda is that of undocumented immigration; that is, the movement of people across international boundaries without proper documentation. Although undocumented immigration to the United States has remained fairly stable since 2009, there are approximately 11 million undocumented immigrants in the United States, with the majority being of Mexican origin (Passel & D’Vera, 2016). Also, there are approximately 4.5 million U.S.-born children whose parents are undocumented, and at least 9 million Latinxs living in “mixed-status” families, where at least one member is undocumented (Taylor, Lopez, Passel, & Motel, 2011). In this study and consistent with linguistic trends, we used the term Latinxs as a gender-inclusive alternative to refer to men and women of Latin American descent (Ramirez & Zeba, 2016). Also, important to note is that although some undocumented immigrants eventually return to their home country, many establish permanent residence in the United States (Passel & D’Vera, 2016).
Undocumented immigration to the United States often presents with multiple stressors and contextual challenges, which may increase risk for mental disorders (Garcini et al., 2016). For instance, physical, verbal, psychological and sexual violence is widespread among undocumented immigrants (Garcini et al., 2016). Also, common to the undocumented experience is discrimination, stigmatization, marginalization, isolation, fear of deportation, exploitability, victimization, living in unsafe neighborhoods, and socioeconomic disadvantage (Abrego, 2006; Garcini et al., 2016; Infante, Idrovo, Sánchez-Domínguez, Vinhas, & González-Vázquez, 2012). In addition to the aforementioned stressors, undocumented immigrants often face intrapersonal and interpersonal stressors (e.g., identity shift, deception, distancing from family) and acculturative stress, which over time increases the risk for mental disorders (Garcini et al., 2016). According to the acculturation literature, the health advantage that is often observed among foreign-born Latinxs, often dissipates with longer time living in the United States, and this may be particularly true for undocumented Latinx immigrants given the chronic exposure that these immigrants face to the aforementioned stressors (Garcini et al., 2016).
Research to inform the mental health of undocumented immigrants is limited and existing studies often lack scientific rigor (Garcini et al., 2016). Overreliance on nonprobability sampling, the use of self-report, imprecise measurement, and the limited analysis of mental health outcomes by immigration legal status have made it challenging to identify the prevalence of mental disorders among undocumented Latinx immigrants. Nevertheless, qualitative research shows depression to be a relevant concern in this population, as well as anxiety and somatization (Garcini et al., 2016). Quantitative studies to document the prevalence of the aforementioned disorders among undocumented Latinx immigrants are needed to inform policy efforts and the development of interventions.
Purpose of StudyThis study aimed to: (a) provide population-based estimates for the current prevalence of mental disorders, including substance use, among undocumented Mexican immigrants residing near the California–Mexico border; (b) assess for relevant comorbidities; and (c) identify sociodemographic, immigration and contextual vulnerabilities associated with meeting criteria for a disorder (including substance use).
Method Design and Sample
This cross-sectional study used Respondent Driven Sampling (RDS) as a sampling and data analysis method. RDS is a methodology based on a mathematical model of the social networks that connect participants in a study, and is currently the most effective method to study hidden populations (Heckathorn, 1997). RDS relies on a structured referral system that uses successive waves of participant recruitment to achieve diversity so that initial samples no longer mirror later samples, which is referred to as equilibrium. To reduce biased estimates, RDS modifies commonly used chain-referral methods in three ways: (a) to increase the breadth of the social network captured by the sample, recruitment is limited by the use of coupons so participants are only allowed a fixed number of referrals (maximum of three); (b) in using referral coupons, participants do not personally identify referrals to the researcher so that anonymity is maintained; and (c) to make results representative of the target population (and not just respondents with large social networks), a systematic weighting scheme is built into the RDS model. Specifically, weights are based on the respondent’s social network size; that is, based on their probability of being captured by this survey technique as well as other features of their social network, which can affect the referral process. In other words, the probability of selection is based on each participant’s probability proportional to the size of his or her social network, which is carefully assessed using specific questions during data collection. In this way, each participant is weighted by the inverse of its probability of selection so that units with small chance of being selected (those with smaller social networks) have more weight, whereas those with larger social networks are assigned less weight in the analyses (Tyldum & Johnston, 2014). Thus, although RDS begins with a convenience sample of undocumented immigrants, a structured process is used in recruitment to obtain unbiased estimates of the undocumented population in the study location. RDS has been previously used to obtain prevalence estimates to inform the health needs of migrant populations, including undocumented immigrants in the United States (Tyldum & Johnston, 2014; Zhang, 2012).
Inclusion criteria for this study was being 18 years or older, Latinx, Spanish-speaking, and undocumented, and not exhibiting acute psychotic symptoms (i.e., hallucinations, delusions, disorganized speech/thought) given questionable capacity to provide adequate informed consent for participation. Only participants of Mexican origin were included in this study. Each respondent was compensated $30 for participation in the assessment and received $10 (for a maximum total of $30) for each recruited peer who met eligibility criteria and participated in the study. Informed consent was obtained prior to participation, and the study received approval by the SDSU/UCSD Institutional Review Board.
Data Collection
Data were collected from November 2014 to January 2015. Recruitment began with three previously selected undocumented Latinx immigrants or seeds. Seeds are nonrandomly selected members of the survey population who initiate the RDS recruitment process (Tyldum & Johnston, 2014). No additional seeds were used in this study. Seeds were selected based on information gathered during formative research, which included focus groups and in-depth interviews with key informants working the target community (Tyldum & Johnston, 2014). Seeds were selected to represent the diversity of the community, including gender, age, place of residence, and relevant immigration characteristics. From each seed, a recruitment chain began so that each seed was provided with three referral coupons to recruit other undocumented Latinx immigrants for participation. The next waves of recruits were provided with another set of three referral coupons to recruit additional participants. Each referral coupon was coded to match the recruiter to the respondent and collected by the interviewer from each respondent in order to link respondent to seeds and referral chains. Sampling continued until the desired sample size was reached and equilibrium achieved. Equilibrium was verified empirically through the use of RDS Analyst (Handcock, Fellows, & Gile, 2014), which showed that the final subjects recruited no longer had identical characteristics to the initial seeds. Figure 1 illustrates the recruitment tree.
Figure 1. Recruitment tree. See the artionlinecle for the color version of this figure.
To collect the data, face-to-face semistructured clinical interviews in Spanish were conducted by native Spanish-speaking psychology trainees working under direct supervision of mental health clinicians. In addition to completing comprehensive training for conducting the aforementioned interviews, all interviewers had extensive personal experience and background knowledge working with undocumented immigrants. Interview duration ranged from 1 to 3 hours depending on the extent of psychological distress reported. To minimize error and increase efficiency, data were collected using a computer assisted personal interviewing system (CAPI; Questionnaire Development System V. 3.0, Nova Research, Silver Spring, MD). All interviews were conducted at a convenient and private location identified during formative research. Participants included 257 undocumented immigrants; however, six participants were not of Mexican origin and three participants had missing data—thus, they were not included in this study. This study is based on data from 248 undocumented Mexican immigrants residing in a medium-size city located in North San Diego County, relatively near the California–Mexico border.
The target area for this study was chosen based on results from formative research. Given that the target area is listed among the most conservative U.S. cities with strong opposition and punitive actions against undocumented immigrants, this area is considered a high-risk area for undocumented immigrants (Bay Area Center for Voting Research, 2005). Of 147,095 individuals residing in the target area, it is estimated that more than 15% are undocumented (Hill & Johnson, 2011). To provide the most conservative estimates, analyses in this study were conducted using the 15% population estimate as reference (N = 22,000).
Measures
Mental and substance use disorders
Current prevalence of mental and substance use disorders was assessed using the M.I.N.I. Mini International Neuropsychiatric Interview V. 6.0 (Lecrubier et al., 1997; Sheehan et al., 1998). The M.I.N.I. is a short, structured diagnostic interview used widely in clinical and research settings to assess for DSM and ICD psychiatric disorders. In this study, specific modules of the M.I.N.I. included: (a) Somatization Disorder; (b) Major Depressive Disorder (MDD); (c) Panic Disorder; (d) Generalized Anxiety Disorder (GAD); and (e) Posttraumatic Stress Disorder (PTSD). Two additional modules were used to assess for current substance use disorders, including alcohol and drug dependence/abuse. At the beginning of each module, screening questions using a dichotomous format (yes/no) corresponding to the main criteria of a disorder were assessed and presented in a gray box. If the participant met the screening criteria, the interviewer proceeded to ask subsequent questions to assess for the disorder using dichotomous responses (yes/no). At the end of each module, a diagnostic box permitted the interviewer to indicate whether diagnostic criteria for a disorder was met. The M.I.N.I. has been previously validated against the Structured Clinical Interview for DSM diagnosis (SCID) and the Composite International Diagnostic Interview (CIDI) for IDC diagnosis (Lecrubier et al., 1997; Sheehan et al., 1998). The M.I.N.I. was selected for use in this study because it has been identified as a more time-efficient alternative to the SCID and CIDI given that the interview can be completed in approximately 15 min. The Spanish version of the M.I.N.I. was used in this study, which is considered to have adequate psychometric properties and is recommended for use with Latinx populations, including those of Mexican origin (Mestre, Rossi, & Torrens, 2013). To further ensure appropriateness of the Spanish language, facilitate comprehension, and to assess for clinical relevance, all modules used were adapted based on results from pilot testing (Peña, Garcini, Gutierrez, Ulibarri, & Klonoff, 2017).
Sociodemographics
Questions were previously translated and validated in Spanish for use with the target population from the 2009 San Diego Prevention Research Center (SDPRC) and the San Diego Labor Trafficking Survey Questionnaire (Zhang, 2012). Categorical demographic questions included sex (0 = male; 1 = female), age (0 = 18 to 25 years; 1 = 26 to 35 years; 2 = 36 to 45 years; 3 = 46 and older), marital status (0 = single; 1 = married/living as married), educational attainment (0 = lower than high school; 1 = some high school and above), and monthly household income (0 = less than $2,000; 1 = $2,000 or greater).
Immigration characteristics
Questions were previously translated and validated in Spanish for use with the target population from the 2009 San Diego Prevention Research Center (SDPRC) and the San Diego Labor Trafficking Survey Questionnaire (Zhang, 2012). These included living in a mixed status family (0 = No; 1 = Yes), place where the immigrant has lived the most (0 = Mexico; 1 = United States; 2 = equally in both) and time in the United States (0 = less than 10 years; 1 = 10 to 20 years; 2 = more than 20 years).
Contextual influences
Questions were comprised of traumatic events and distress from postmigration living difficulties. History of traumatic events was assessed using an adapted version in Spanish of the traumatic events inventory of the Harvard Trauma Questionnaire (HTQ; Beaton, Bombardier, Guillemin, & Ferraz, 2000; Mollica, McDonald, Massagli, & Silove, 2004). This adapted version consisted of a 25-item inventory assessing traumatic events across seven domains: (a) material deprivation; (b) war-like conditions; (c) bodily injury; (d) forced confinement/coercion; (e) forced to harm others; (f) disappearance/death/injury of loved ones; and (g) witnessing violence to others (Mollica et al., 2004). Based on results from pilot testing, two additional items were added to the inventory (i.e., history of deportation and domestic violence). Responses to each of the traumatic events were dichotomous (0 = no; 1 = yes). In our study, the Cronbach’s alpha for the HTQ was 0.83.
Distress from postmigration living difficulties was assessed using the Post-Migration Living Difficulties (PMLD) Questionnaire (Aragona, Pucci, Mazzetti, & Geraci, 2012). The PMLD is a 27-item inventory used to assess recent adverse life experiences related to immigration, as well as distress associated with such experiences. The PMLD measures postmigration difficulties along six domains: (a) finances/employment; (b) family and relationships; (c) access to health care; (d) discrimination/marginalization; (e) acculturation; and (f) stressors unique to undocumented status (e.g., fear of deportation, inability to go to Mexico in case of emergency, trouble with immigration officials). Participants rated their distress associated with each PMLD using a 4-point Likert scale from 0 = not stressful to 3 = extremely stressful. The PMLD renders a total PMLD mean distress score, as well as a mean distress score for each of the PMLD domains. For this study, the total PMLD mean distress score was used. In our study, the Cronbach’s alpha for the PMLD was 0.86. The HTQ and the PMLD were previously translated and validated in Spanish for use with Latinx populations, and were adapted for linguistic accuracy with the target population based on results from pilot testing (Peña et al., 2017).
RDS questions
Three questions were used for mapping recruitment and to calculate RDS weights: (a) the estimated size of the respondent’s personal network that is undocumented; (b) relationship to the referral source; and (c) length of time knowing the referral source. These questions were previously translated and validated in Spanish for use with the target population from the San Diego Labor Trafficking Survey Questionnaire (Zhang, 2012).
Statistical Analyses
To estimate the sample size needed, a priori power analysis was conducted using OpenEpi, Version 3.01 (Dean, Sullivan, & Soe, 2013). Based on the prevalence of mental disorders among Mexican-origin foreign-born immigrants (Alegría et al., 2008) and to detect prevalence at 14% within a 95% confidence interval at 7% precision and with a design effect of 2 (Salganik, 2006), a sample size of 190 participants was needed, which was exceeded. For all analyses, inferential statistics accounted for design effects and sample weights to produce weighted estimates. Descriptive statistics and weighted frequencies along with 95% confidence intervals were calculated to assess for the current prevalence of mental disorders, including substance use. Chi-square statistics and analysis of variance were used in bivariate analyses (p < .05). Standardized residuals were used in post hoc comparisons for variables with more than two categories (Siegel, 1988). To identify vulnerabilities associated with meeting criteria for a disorder, logistic regression was used. For parsimony and to meet recommendations for the minimum number of events per variable (EPV) required in multivariate analysis (Peduzzi, Concato, Kemper, Holford, & Feinstein, 1996), only variables significantly associated with the outcome of interest in bivariate analyses were included in the multivariate model. All tests were set at p < .05.
RDS assumptions and weights
For the testing of RDS assumptions, generation of RDS weights, and analysis of population estimates, RDS Analyst was used (Handcock et al., 2014). A diagnostic testing for RDS assumptions showed that the sample reached equilibrium at the 11th wave of recruitment, met basic RDS assumptions, and showed little homophily bias. Homophily refers to the tendency of individuals to associate with similar people at a higher rate than between dissimilar people (Volz & Heckathorn, 2008). Thus, an evaluation of RDS assumptions in this study suggested that the characteristics of the recruited, weighted sample approximate the characteristics of the larger networks of undocumented Mexican immigrants in the target area (midsize population estimate N = 22,000).
Results Population Characteristics
The average age was 38 years (SD = 11.2). Most participants were female (69%), married (68%), had lower than a high school education (65%), and lived on a monthly household income of less than 2,000 USD (66%). The majority of participants had lived in the United States for more than 10 years (M = 16 years; SD = 7.9), most had lived most of their life in Mexico (66%), and the majority lived in mixed status families (73%). Also, most reported a history of trauma (83%) and have faced several postmigration living difficulties (M = 14; SD = 5.6; see Table 1).
Demographic, Immigration, and Contextual Characteristics by History of Meeting Criteria for Any Disorder
Current Prevalence of Mental and Substance Use Disorders
Overall, 23.1% of participants met criteria for one or more of the assessed disorders, (95% CI = 17.1; 29.0). Specifically, 21.6% of immigrants met criteria for a current mental disorder (95% CI = 16.0; 27.2). The most prevalent mental disorders were MDD (14.4%, 95% CI = 10.2; 18.6), panic disorder (8.4%, 95% CI = 5.0; 11.9) and GAD (6.6%, 95% CI = 3.4; 9.8; see Table 2). Moreover, 3.7% (95% CI = 1.2; 6.1) of immigrants met criteria for a substance use disorder (See Table 3).
Population-Base Estimates for Current Prevalence of Relevant Mental Disorders (Not Including Substance Use) Among Undocumented Mexican Immigrants by Demographic, Immigration, and Contextual Vulnerabilities
Population-Base Estimates for Current Prevalence of Substance Use Disorders Among Undocumented Mexican Immigrants by Demographic, Immigration, and Contextual Vulnerabilities
Comorbidity of Mental and Substance Use Disorders
The highest comorbidity reported were between MDD and panic disorder (81.2%), PTSD and MDD (62.5%), PTSD and panic disorder (50.0%), GAD and MDD (43.8%), PTSD and GAD (37.5%), and panic disorder and GAD (27.3%). Pertaining to substance use, the highest comorbidity reported were between having a substance use disorder and MDD (30.0%), and having a substance use disorder and GAD (10.0%).
Vulnerabilities Associated With Current Prevalence of a Disorder
Bivariate analyses showed that significant differences in meeting criteria for having a current disorder were found across age groups, marital status, history of traumatic events, and distress associated with PMLD. Specifically, when compared to other age groups, a higher proportion of immigrants ages 18 to 25 and those ages 46 and older met criteria for a disorder (p < .05). When compared to their married or living as married counterparts, a higher proportion of single participants met criteria for a disorder (p < .05). Meeting criteria for a disorder was more prevalent among those with a history of traumatic events (p < .01) and those with greater distress from PMLD (p < .001; see Table 2).
In multivariate analyses, the full model containing age, marital status, history of trauma, and mean distress from PMLD was statistically significant, χ2(6, N = 246) = 42.54, p < .001. The model fit using Cox and Snell R square was 0.16 and 0.24 when using Nagelkerke R squared. The model correctly classified 80.1% of cases. As shown in Table 4, age and mean distress from PMLD made a statistically significant contribution to the model. Specifically, after controlling for all other factors in the model, younger immigrants (18 to 25 years) were 3.7 and 2.7 times more likely to meet criteria for a disorder than their 26 to 35 years and 36 to 45-year-old counterparts, respectively (p = .03; p = .04). No significant difference in meeting criteria for a disorder was observed between the younger group (18–25 years) and the oldest group (46 years and older). Also, for each unit increase in mean distress from PMLD, participants were 4.0 times more likely to meet criteria for a disorder (p < .01; see Table 4).
Multivariate Logistic Regression Predicting Likelihood for Meeting Criteria for a Current Disorder
Post hoc analyses were conducted to identify specific sources of distress across PMLD domains associated with meeting criteria for a disorder. Results showed that distress from perceived discrimination was significantly associated with meeting criteria for a disorder (p < .01). More specifically, for each unit increase in mean distress from discrimination, participants were 2.7 times more likely to meet criteria for a disorder, particularly MDD (OR-2.57, p = .012).
DiscussionTo our knowledge, this is the first study to provide population-based estimates for the prevalence of current mental and substance use disorders among undocumented Mexican immigrants residing near the California–Mexico border. Overall, about a quarter of undocumented Mexican immigrants in our study met criteria for one or more of the assessed disorders, with the most prevalent disorders being MDD and anxiety disorders. Based on results from the National Comorbidity Survey Replication (NCSR), the estimates obtained in our study for MDD, GAD, and panic disorder were considerably higher in this immigrant community when compared to those for the general U.S. population (Kessler, Chiu, Demler, Merikangas, & Walters, 2005). For instance, the current prevalence of MDD in the NCSR was approximately 7%, whereas it was 14% in our study. Likewise, prevalence of panic disorder and GAD in the NCSR were approximately 3%, whereas it was 8% and 7%, respectively, in our study. Although the “immigrant paradox” suggests that despite the stressful experiences and socioeconomic disadvantage associated with immigration, foreign-born status protects against mental disorders (Burnam, Hough, Karno, Escobar, & Telles, 1987), the aforementioned findings suggests that this may not hold truth for undocumented Mexican immigrants.
Another important finding in our study was the low prevalence of PTSD in this immigrant population, which is comparable to the prevalence of PTSD in the general U.S. population (3.5% in the NCRS vs. 3.0% in our study; Burnam et al., 1987). This is surprising given the high prevalence of traumatic events reported in our study (83%). Although underreporting may provide a possible explanation for the low prevalence of PTSD found in our study, previous research suggests that traditional criteria for PTSD may not entirely capture psychological distress and symptom presentation associated to trauma among nonwestern cultures (Hinton & Kirmayer, 2013; Hobfoll, 2014). Additional studies are needed to facilitate an understanding as to how meeting criteria for PTSD may or may not adequately capture distress associated to trauma among undocumented Mexican immigrants.
Pertaining to substance use disorders, the prevalence for having a substance use disorder in this immigrant population was comparable to that of the U.S. general household population (3.8% in the NCRS vs. 3.7% in our study). The prevalence of substance use disorders found in our study is consistent with previous qualitative research showing that undocumented immigrants are unlikely to engage in substance use given that it increases risk for deportation and it interferes with productivity at work (Garcini et al., 2014). This finding is important given that it defies existing stereotypes that contribute to stigmatization and discrimination of undocumented Mexican immigrants as a population with high prevalence of substance use (Cohen & Chavez, 2013; Niles et al., 2015).
Another aim of our study was to identify vulnerabilities associated with having a disorder in this immigrant population. An important finding was that significant differences in the prevalence of having a disorder were observed across age groups, with younger immigrants (18 to 25 years) being more likely to meet criteria for a disorder than their 26–46-year-old counterparts, but not those ages 46 and older. Post hoc analyses showed that the majority of this younger age group was brought to the United States as children (92.3%); thus, it is possible that this age group is most representative of undocumented immigrants that are often referred to as DREAMers, who are educated, undocumented young adults that were brought to the United States as children, and who may qualify for the requirements of the Development Relief and Education for Alien Minors (DREAM) Act. Under this act, DREAMers are given a time-limited conditional permit to remain in the United States and pursue an education, with the caveat of facing constant institutional and societal exclusion and rejection due to their undocumented status (Abrego, 2006; Ellis & Chen, 2013). Research shows that this “double standard” of living increases risk for psychological distress in this younger subgroup (Pérez, Cortés, Ramos, & Coronado, 2010). Similarly, a high prevalence of meeting criteria for a disorder was observed among those 46 years and older. This is not surprising given that older undocumented immigrants may be susceptible to distress from age-related illnesses and disability without access to health care, difficulties finding and keeping employment, and longer time away from their families in Mexico, which in turn may increase risk for a mental disorder. Additional studies are needed to identify vulnerabilities associated with risk for mental disorders among younger and older undocumented immigrants to inform policy and intervention efforts.
Postmigration difficulties, particularly discrimination, were associated with meeting criteria for a disorder. Conflicting political views and growing animosity among people of different backgrounds have recently brought discrimination against undocumented immigrants to the forefront of the U.S. political, economic, and social landscape (Pew Research Center, 2016). The negative effects of discrimination on mental health are well documented, with discrimination being consistently associated with a greater risk for mental disorders including depression and anxiety (Pascoe & Smart Richman, 2009). Very little is known about the effects of discrimination on the well-being of undocumented immigrants, as well as protective factors and ways for coping that could ameliorate its undesirable effects (Abraído-Lanza, Echeverría, & Flórez, 2016; Berkel et al., 2010). Thus, our findings highlight the relevance of studying the complex cultural and sociopolitical realities faced by undocumented Mexican immigrants, including experiences of discrimination, as to inform context-sensitive mental health interventions and policies (Abraído-Lanza et al., 2016; Berkel et al., 2010).
Limitations
Our study makes a timely and significant contribution to identify the prevalence of relevant mental health disorders in this hard-to-reach population. Regardless, this study has limitations. First, RDS has been identified as the most effective method to study hidden populations (Heckathorn, 1997); however, it is not free from methodological limitations (Goel & Salganik, 2010). Nevertheless, several steps were taken in this study to aim for collecting data from a representative sample (i.e., formative research, preselection of diverse seeds, long recruitment chains, use of weighted estimates based on size of social network, accurate assessment of social network size). Also, it is possible that distress and mental health disorders in this community may be different than those experienced by undocumented immigrants from other countries (e.g., Central America) and who reside in other parts of the United States. Follow-up studies with different populations of undocumented immigrants and with undocumented immigrants residing in other regions of the United States, including other regions across the U.S–Mexico border, are warranted. In addition, our sample was predominately female and on average had lived in the United States for more than 10 years. Thus, our data is most representative of undocumented Mexican women who have made the United States their home and most of which are living in mixed status families, where some family members are U.S. citizens. Moreover, individuals with certain serious mental illnesses (e.g., schizophrenia) were excluded from participation; thus, estimates for certain disorders such as substance use, may be higher among undocumented immigrants with such disorders. Also, the information gathered was based on retrospective reporting, which may lead to biases and lower estimates than contemporaneous reporting (Brewin, Andrews, & Gotlib, 1993). Thus, it is likely that underreporting may have occurred and that the estimates provided in this study may be higher. Finally, this study was cross-sectional; thus, causation cannot be inferred.
Conclusion
Overall, our findings have important public health and clinical implications, including the need for the development and provision of context- and culture-sensitive interventions. Unfortunately, there are many barriers to mental health service use for undocumented immigrants including stigma, fear of deportation, cost, limited information, and restricted access to health care (Garcini et al., 2016). Debates on programs and policies pertaining to undocumented immigrants are complex and multifaceted, and divisiveness on immigration and welfare reform in the United States is long-standing. Revisiting policies to devise solutions grounded in evidence and developing new alternatives to facilitate access and provision of mental health services to this at-risk population is critical to protect their human rights and reduce mental health disparities in this community.
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Submitted: March 14, 2017 Revised: June 19, 2017 Accepted: June 22, 2017
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Source: Journal of Consulting and Clinical Psychology. Vol. 85. (10), Oct, 2017 pp. 927-936)
Accession Number: 2017-42717-001
Digital Object Identifier: 10.1037/ccp0000237
Record: 31- Title:
- Movement abnormalities predict conversion to Axis I psychosis among prodromal adolescents.
- Authors:
- Mittal, Vijay A.. Department of Psychology, Emory University, Atlanta, GA, US, vmittal@emory.edu
Walker, Elaine F.. Department of Psychology, Emory University, Atlanta, GA, US - Address:
- Mittal, Vijay A., Department of Psychology, Emory University, Psychological Center, 235 Dental Building, 1462 Clifton Road, Atlanta, GA, US, 30322, vmittal@emory.edu
- Source:
- Journal of Abnormal Psychology, Vol 116(4), Nov, 2007. pp. 796-803.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 8
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- schizophrenia, prodromal adolescents, movement abnormality, conversion, psychosis
- Abstract:
- Evidence suggests that movement abnormalities are a precursor of psychosis. The link between movement abnormalities and psychotic disorders is presumed to reflect common neural mechanisms that influence both motor functions and vulnerability to psychosis. The authors coded movement abnormalities from videotapes of 40 adolescents at risk for psychosis (designated prodromal on the Structured Interview for Prodromal Symptoms; T. J. Miller et al., 2002). Following initial assessment, participants were evaluated for diagnostic status at 4 times annually. Ten participants converted to an Axis I psychosis (e.g., schizophrenia) over the 4-year period. Comparisons of converted and nonconverted participants at baseline indicated that the groups did not differ on demographic characteristics or levels of prodromal symptomatology, but those who converted exhibited significantly more movement abnormalities. Movement abnormalities and prodromal symptoms were strongly associated and logistic regression analyses indicated that abnormalities in the face and upper body regions were most predictive of conversion. Findings suggest that individuals with elevated movement abnormalities may represent a subgroup of prodromal adolescents who are at the highest risk for conversion. The implications for neural mechanisms and for identifying candidates for preventive intervention are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Conversion Disorder; *Movement Disorders; *Prodrome; *Psychosis; Schizophrenia
- Medical Subject Headings (MeSH):
- Adolescent; Child; Diagnostic and Statistical Manual of Mental Disorders; Female; Humans; Male; Movement Disorders; Predictive Value of Tests; Prospective Studies; Psychotic Disorders
- PsycINFO Classification:
- Schizophrenia & Psychotic States (3213)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Childhood (birth-12 yrs)
School Age (6-12 yrs)
Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Dyskinesia Identification System: Condensed User Scale
Structured Clinical Interview for DSM-IV Axis I Disorders - Grant Sponsorship:
- Sponsor: National Institute of Mental Health
Grant Number: RO1-MH4062066
Recipients: Walker, Elaine F. - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Mar 12, 2007; Revised: Mar 12, 2007; First Submitted: Dec 11, 2006
- Release Date:
- 20071119
- Correction Date:
- 20120312
- Copyright:
- American Psychological Association. 2007
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/0021-843X.116.4.796
- PMID:
- 18020725
- Accession Number:
- 2007-17062-012
- Number of Citations in Source:
- 41
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2007-17062-012&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2007-17062-012&site=ehost-live">Movement abnormalities predict conversion to Axis I psychosis among prodromal adolescents.</A>
- Database:
- PsycINFO
Movement Abnormalities Predict Conversion to Axis I Psychosis Among Prodromal Adolescents
By: Vijay A. Mittal
Department of Psychology, Emory University;
Elaine F. Walker
Department of Psychology, Emory University
Acknowledgement: This research was supported by National Institute of Mental Health Grant RO1-MH4062066 to Elaine F. Walker.
The modal period for the onset of psychosis is early adulthood, with many patients manifesting behavioral dysfunction during adolescence (Cannon, Rosso, Bearden, Sanchez, & Hadley, 1999; Neumann & Walker, 2003). The premorbid indicators include behavioral signs, such as social withdrawal, and thought abnormalities (Walker, Baum, & Diforio, 1998), deficits in memory and executive function (Silverstein, Mavrolefteros, & Turnbull, 2003), and neuromotor abnormalities (Neumann & Walker, 2003). Because these signs often become more pronounced as the individual approaches young adulthood (Cornblatt, Lencz, & Obuchowski, 2002), it is assumed that the heightened risk associated with this developmental period results, in part, from neuromaturational processes (Walker & Diforio, 1997).
The prodromal period is typically defined as a period of functional decline that precedes the onset of psychosis (Larsen, McGlashan, Johannessen, & Vibe-Hansen, 1996). Instruments have been developed to measure the prodrome, and recent research has demonstrated that 13% to 40% of individuals who meet current criteria for the prodrome meet diagnostic criteria for schizophrenia or affective psychosis within 2 years (Haroun, Dunn, Haroun, & Cadenhead, 2006). The prodrome represents both a viable point for intervention and a developmental period with strong potential to shed light on the etiology of schizophrenia and affective disorders with psychotic features (i.e., schizoaffective disorder, bipolar disorder, and unipolar depression; Haroun et al., 2006). Within this context, premorbid movement abnormalities are of particular interest because they are more pronounced in prodromal individuals (Mittal, Dhruv, Tessner, Walder, & Walker, 2007; Mittal, Tessner, et al., 2007), independent of treatment with psychotropic medication (Boks, Liddle, Burgerhof, Knegtering, & van den Bosch, 2004; Puri, Barnes, Chapman, Hutton, & Joyce, 1999). For example, longitudinal studies have shown that children who later develop psychotic disorders manifest a higher frequency of movement abnormalities when compared to those with healthy adult outcomes (Fish, Marcus, Hans, Auerbach, & Purdue, 1992; Schiffman et al., 2004). This relationship has also been reflected in brain structure; childhood motor abnormalities are linked with greater ventricular enlargement in adult patients with psychosis (Walker, Lewine, & Neumann, 1996). Further, movement abnormalities—especially dyskinesias of the face and upper limbs—are more common in adult patients with psychosis, even patients who have never received antipsychotic medications (Gervin et al., 1998; Puri et al., 1999).
The mechanisms underlying the relation between movement abnormalities and psychosis have been of interest to investigators because the neurocircuitry hypothesized to be implicated in psychotic symptoms is partially shared by the circuits that are known to give rise to dyskinetic movements (Gray, Kumari, Lawrence, & Young, 1999; Graybiel, 1997; Walker, 1994). Specifically, overactivation of dopaminergic pathways in striatal regions (proximal to the region regulating hyperkinesias) has been hypothesized to contribute to psychotic symptoms (Graybiel, 1997; Walker, 1994). Thus similar circuitry malfunctions may result in two manifestations: movement abnormality and psychotic symptoms (Walker, Lewine, & Neumann, 1996). Thus it is possible that for some individuals, movement abnormalities are the first signs of compromised neurocircuitry that may later give rise to psychosis (Mittal, Tessner, et al., 2007).
As it is becoming increasingly important to identify youth who are most likely to benefit from preventive intervention (Haroun et al., 2006), research examining potential predictive markers is needed. There have been no previous reports on the potential of movement abnormalities to predict conversion to Axis I disorders in prodromal individuals. But the existing literature and theory on the relation between movement abnormalities and psychoses provides a basis for hypothesizing an association. The present investigation tests the hypothesis that, among prodromal adolescents, movement abnormalities will predict conversion to psychosis. To test this hypothesis, prodromal adolescents were assessed for movement abnormalities and evaluated for psychiatric status over a period of 4 years.
Diagnostic Specificity of Outcome for Prodromal IndividualsResearch has indicated that individuals identified as prodromal using current criteria are at increased risk for developing schizophrenia as well as affective disorders with psychotic features (Haroun et al., 2006; Miller et al., 2002; Yung et al., 1998). These findings are consistent with genetic evidence of shared etiological factors among psychotic disorders defined by the Diagnostic and Statistical Manual of Mental Disorders (DSM–IV; American Psychiatric Association, 1994) (Cardno, Rijsdijk, Sham, Murray, & McGuffin, 2002; Riley & Kendler, 2006). As a result, prodromal researchers are focusing on psychosis as the outcome variable. In an effort to provide consistency with the growing prodromal research literature, the present study examines DSM–IV Axis I psychosis, including both schizophrenia and affective disorder with psychotic features. However, because it is also of interest to determine whether the movement abnormalities under investigation are more predictive of schizophrenia in particular, compared with affective psychosis, the study includes comparative analyses of both diagnostic outcomes.
Method Participants
Participants were recruited for a prospective study of at-risk adolescents conducted at Emory University. Adolescents from the Atlanta area were recruited through announcements, describing prodromal symptoms in lay terminology, directed at parents. This study presents data on 40 adolescents, ranging in age from 12 to 18 years (M = 14.38, SD = 1.73), who underwent an initial assessment and three annual follow-up assessments.
Assent and written consent was obtained from all participants and a parent, in accordance with the guidelines of the Emory University Human Subjects Review Committee. Demographic characteristics of the sample are presented in Table 1. Exclusion criteria were neurological disorder, mental retardation, substance addiction (DSM–IV criteria for a substance disorder), and current Axis I disorder. In the present study, it was not necessary to exclude any potential prodromal participants due to substance abuse.
Demographic and Clinical Characteristics of Sample
Although priority was given to the recruitment of participants who had never received a psychotropic drug, a subgroup of participants was on one or more psychotropics. This reflects national trends, in that there has been a significant increase in the number of children—especially adolescents with adjustment problems—who are taking psychotropic medications (Zito et al., 2003). The psychotropic drugs for which increased prescriptions to children have been most clearly documented are stimulants, antidepressants, and to a lesser extent, antipsychotics. This trend was apparent in the present sample; the most common psychotropic was stimulants (27%), followed by antidepressants (25%) and antipsychotics (14%). Most psychotropic medications had been prescribed by pediatricians, off-label, with antipsychotics primarily directed at controlling conduct problems rather than treating psychotic symptoms. Because these medications can affect movement abnormalities, medication status was treated as a covariate in all statistical analyses.
Procedure
Assessing symptomatology
Participants in the present study met criteria for attenuated positive symptom syndrome (Miller et al., 2002), defined by the presence of moderate to severe positive symptoms. The Structured Interview for Prodromal Symptoms (Miller et al., 2002) was administered to obtain data on prodromal signs during the initial assessment. The Structured Interview for Prodromal Symptoms contains an instrument, the Scale of Prodromal Symptoms, which rates the severity of relevant symptoms along dimensions ranging from healthy to pathological. The Scale of Prodromal Symptoms is comprised of five symptom domains that are classified as positive (unusual thoughts or ideas, suspiciousness, grandiosity, perceptual abnormalities, conceptual disorganization); negative (social isolation, avolition, decreased expression of emotion, decreased experience of emotion, decreased ideational richness, deteriorated role function); disorganized (odd behavior, bizarre thinking, trouble with focus and attention, impairment in personal hygiene or social attention); and general (sleep disturbance, dysphoric mood, impaired stress tolerance, and motor disturbance—in the present study, to avoid overlap with the movement ratings, the motor disturbance item was not included in the ratings of prodromal status). Although all subjects met the symptom severity criteria for attenuated positive symptom syndrome, the duration criteria could not be established for all participants and were not considered a central focus in the present study. Each symptom was rated on a 6-point scale that ranged from absent to severe. The mean of the combined category scores was used as an indicator of global symptomatology.
To assess for the presence of Axis I disorders, the Structured Clinical Interview for Axis I DSM–IV Disorders (First, Spitzer, Gibbon, & Williams, 1995) was administered during the initial evaluation and subsequent yearly follow-up assessments (for a 4-year period). The Structured Clinical Interview for Axis I DSM-IV Disorders has been demonstrated to have excellent interrater reliability in adolescent populations (Martin, Pollock, Bukstein, & Lynch, 2000) and has been used in several studies that focused on adolescent populations with schizophrenia spectrum disorders (Mittal et al., 2006; Walker, Lewis, Loewy, & Palyo, 1999; Weinstein, Diforio, Schiffman, Walker, & Bonsall, 1999). The Axis I psychotic disorders identified in the study sample are listed in Table 2.
Characteristics of Participants Who Converted From Prodromal Status to Axis I Psychotic Disorder
Interviews were conducted by either Elaine F. Walker (a clinical psychologist) or an advanced (4th year or beyond) psychology doctoral student. Training of interviewers was conducted over a 2-month period, and interrater reliabilities exceeded the minimum study criterion of .80 (Pearson correlation). All interviews were videotaped throughout the course of the study so that interrater reliability could be monitored. Videotapes were reviewed by Elaine F. Walker and/or a psychiatrist to confirm diagnostic reliability.
Coding of movement abnormalities
The Dyskinesia Identification System: Condensed User Scale (DISCUS; Kalachnik, Young, & Offerman, 1984) was used to code involuntary movements. The empirically developed DISCUS contains 15 items rated on a 0–4 (absent to severe) scale and employs a methodology that uses six different quality of item indices (Sprague, White, Ullman, & Kalachnik, 1984). The DISCUS was chosen because it yields high interrater reliability (≥ .90) for mentally ill and nonclinical samples (Kalachnik & Sprague, 1993). The measure also yields separate indexes for different body regions: facial (consisting of tics, grimaces, blinking, chewing/lip smacking, puckering/sucking/thrusting lower lip, tongue thrusts, tonic tongue, tongue tremor, athetoid/myokymic/lateral tongue), upper body (retrocollis/torticollis, shoulder/hip torsion, athetoid/myokymic finger–wrist–arm, pill rolling, writhing, and alternating extensions and flexions of the fingers or wrist), and lower body (ankle flexion/foot tapping, toe movement). The sum of movement abnormalities in each body region was used for the present analyses.
Following the procedures used in previous research (Walker et al., 1999), movement abnormalities were coded from videotapes made during the baseline clinical interview. Interviews were conducted in private rooms and the participant was videotaped while seated in a chair facing a wall-mounted camera behind the interviewer. The chair was positioned so that the entire body was visible on tape. A total of 45 min of each videotape was coded. Raters were blind to the participants' clinical status, and coding was conducted without audio. Research assistants were trained in the application of the coding procedures over a 1-month period using tapes of nonparticipants. Coding of the participant tapes began after all pairs of raters had achieved a minimum interrater reliability of .80 for coding (Pearson correlation), independently, each body region and movement type, in a 6-min segment. The mean reliability at the end of the training period was .86, ranging from .72 to .95 across body regions.
ResultsOf the 40 putatively prodromal participants, 10 (25%) converted to an Axis I psychotic disorder during the period of the study (initial assessment followed by three annual assessments). (See Table 2 for a description of the characteristics of the conversion group.) None of the prodromal individuals converted to an Axis I disorder that did not contain psychotic features (this was likely a function of the sampling strategy that focused recruitment on individuals with schizotypal and other symptoms linked with risk for developing psychosis). Analyses were conducted to test for demographic differences between the converted and nonconverted groups. Chi-square tests revealed no significant differences between converters and nonconverters in gender ratio or ethnic distribution, and t tests indicated no group differences in age or level of prodromal symptomatology at baseline. Screening the data using Kolmogorov–Smirnov tests revealed that distributions of movement abnormality variables were normal and met the assumptions for parametric statistics.
Associations Between Movement Abnormalities and Psychotropic Medication
Point-biserial correlations between the movement abnormality region (face, upper body, lower body) and each dummy coded medication class (yes vs. no: stimulant, antidepressant, antipsychotic) were examined to provide a framework with which to interpret the movement findings. There were no significant associations between any of the psychotropic medications and movement scores. It is important to note that prescription medication was not a controlled component of the present study; individuals undergoing medication treatment were observed under a naturalistic paradigm. As such, any interpretations, particularly given the small number of participants being treated (see Table 1), should be made with caution.
Associations Between Movement Abnormalities and Symptoms at Baseline
Relationships between movements and symptoms at baseline were assessed by conducting partial correlation analyses for each of the respective movement regions and global, positive, and negative symptoms rated with the Scale of Prodromal Symptoms. Correlations controlled for the classes of psychotropic medications using the dummy coding procedure. Abnormalities in the face region were significantly and positively associated with the three symptom scores. In the upper body region, negative symptoms were positively associated with movement abnormalities, and global symptomatology showed a strong trend in the same direction. There were no significant associations for lower body scores (see Table 3).
Associations Between Prodromal Symptomatology and Movement Abnormality at Baseline
Movement Abnormalities and Conversion Group Differences
Univariate analysis of covariance (ANCOVA) with medication status (dummy coded: stimulant, antidepressant, and antipsychotic) treated as covariates was used to test for group differences in movement abnormalities. To clarify the relationship of psychotropic medications to movement abnormalities and outcome, the same series of analyses were also conducted without the dummy coded medication covariates in an analysis of variance (ANOVA).
Face region
As predicted, ANCOVA indicated significant differences in movement abnormalities in the facial region between the nonconverted (M = 0.65, SD = 1.20) and the converted groups (M = 2.10, SD = 1.52), F(1, 38) = 2.08, p ≤ .05, η2 = .24. Among the classes of psychotropics, no covariates approached significance. Analyses for the facial region without covariates indicated the same pattern of results for group differences, F(1, 38) = 9.34, p ≤ .01, η2 = .20.
Upper body region
For the upper body region, the ANCOVA indicated that there were significant differences between the nonconverted (M = 2.10, SD = 2.17) and the converted groups (M = 5.10, SD = 3.10), F(1, 38) = 2.84, p ≤ .05, η2 = .25. As with the previous analyses, none of the dummy coded psychotropic medication covariates approached significance. When ANOVAs were conducted, the same pattern of group differences remained significantly different, F(1, 38) = 11.20, p ≤ .01, η2 = .23.
Lower limb region
For the lower limb region, both ANCOVA and ANOVA indicated that there were no significant differences between the nonconverted (M = 1.46, SD = 1.31) and the converted groups (M = 1.60, SD = 1.26). See Figure 1 for an illustration of the mean group differences.
Figure 1. Group differences in movement abnormalities by body region. Movement abnormalities in the face and upper body regions were significantly more frequent in the converted prodromal group (**p ≤ .01). Error bars represent standard error of the mean.
Subtypes of Psychotic Outcomes
In the present study, 3 of the prodromal individuals converted to a schizophrenia outcome and 7 converted to affective illness (bipolar, schizoaffective, or unipolar) with psychotic features (see Table 1). To determine whether the group differences in movement abnormalities were related to one of these outcomes in particular, subgroup comparisons were conducted. ANCOVAs and ANOVAs were conducted for each movement region using three groups: the nonconverted group, a group converted to a schizophrenia outcome (n = 3), and a group converted to affective disorder with psychotic features (n = 7). It is important to note that these are exploratory analyses, and that the low number in the conversion subgroups limits statistical power. As such, results should be interpreted with caution.
Face region
For the face region, omnibus analyses were significant, ANCOVA, F(1, 38) = 3.53, p ≤ .01, η2 = .34 (no covariates were significant); ANOVA, F(1, 38) = 7.22, p ≤ .01, η2 = .28. Post hoc analyses indicated that both converted groups were significantly elevated when compared to the nonconverted prodromal adolescents (p ≤ .05) and the schizophrenia group showed a trend toward exhibiting more movement abnormalities than the group with affective disorder with psychotic features (p = .06).
Upper body region
For the upper body region, omnibus analyses were significant, ANCOVA, F(1, 38) = 4.77, p ≤ .01, η2 = .42, and no covariates were significant; ANOVA was also significant, F(1, 38) = 11.49, p ≤ .01, η2 = .39. Post hoc analyses indicated that the schizophrenia converted group was significantly elevated in comparison to the affective disorder with psychotic features group and the prodromal nonconverted group (p ≤ .05). There was a trend (p = .11) indicating the affective disorder group exhibited elevated movement disorders in comparison to the nonconverted prodromal group.
Lower body region
For the lower body region, omnibus analyses were not significant for ANCOVA (η2 = .21) or ANOVA (η2 = .01). See Table 4 for standard deviations and mean group differences.
Means and Standard Deviations of Movement Abnormalities by Group
Logistic Regression of Movement Abnormality Predicting Conversion
To provide an index of the power of movement abnormalities in predicting conversion, we conducted logistic regression analyses with movement abnormality in the respective region as a continuous predictor variable and conversion status (yes/no) as a dichotomous outcome. For these regression equations, psychotropic medications (dummy coded stimulant, antidepressant, and antipsychotic classes) were entered as control variables. Odds ratios were highly significant for the facial region and the upper body region, suggesting that abnormalities in these regions are strong predictors of conversion (see Table 5 for estimated odds ratios). Results for the lower body region were not significant. The psychotropic medication covariates did not approach significance.
Logistic Regression Results (Odds Ratio) for Prodromal Individuals Who Converted to Axis I Schizophrenia Spectrum Disorders and Mood Disorders With Psychotic Features Compared With Nonconverted Individuals
DiscussionOur finding that 25% of participants converted to Axis I psychosis within a 4-year period is highly similar to the rate reported in other prospective longitudinal studies of prodromal youth (Yung & McGorry, 1996; Yung et al., 1998). Consistent with our hypothesis, the converted group exhibited significantly more movement abnormalities in the face and upper body regions than did the nonconverted group. Thus it appears that the kinds of upper body and face movement abnormalities rated on the DISCUS are most common in prodromal adolescents, who convert to an Axis I disorder within 4 years.
Congruent with our previous research comparing at-risk to healthy control youngsters (Walker et al., 1999), there were no significant group differences in the lower extremities. It is possible that the current methodology, in which individuals were seated during the assessment of movement abnormalities, may confound assessment by limiting movements in the lower extremities. Indeed, the earlier study (Walker et al., 1999) also used a coding procedure with seated individuals. However, consistent with the present findings, other reports examining movement abnormalities have noted a trend for more pronounced movement abnormalities in facial and upper body regions. For example, in a report examining movement abnormalities in adults with psychosis, researchers, using a coding method in which participants were not seated, observed abnormalities in the oral/facial region roughly three times as often—and in the upper body region roughly twice as often—as abnormalities in the lower extremities (Puri, Barnes, Chapman, Hutton, & Joyce, 1999). One potential explanation for the region differences observed in the present investigation lies in the neurological basis of movement abnormalities; because there are topographically organized motor subcircuits for different body regions, it is possible for movement abnormalities to be limited to one area (Walker, 1994).
The present findings support the theory that that hyperkinesias and psychotic symptoms involve shared neural mechanisms (Graybiel, 1997; Mittal, Dhruv, et al., 2007; Mittal, Tessner, et al., 2007; Walker, 1994;). Hyperkinetic movements are assumed to be a reflection of overactivation of ascending dopamine pathways, specifically the striatal pathway mediated by the D2 receptor subtype (Alexander, Crutcher, & Delong, 1990; Smith, 1982). Striatal D2 receptor irregularity has been strongly implicated in Axis I psychosis (Kestler, Walker, & Vega, 2001; Seeman & Kapur, 2005). Given this overlap, some researchers have suggested that cortico–striato–pallido–thalamic circuit malfunction, moderated by dopaminergic function, is responsible for symptomatology that includes deficits in motivation and cognitive functioning (Gray et al., 1999) as well as both positive and negative psychotic symptomatology (Graybiel, 1997; Walker, 1994). If this theory is correct then it may be possible to draw tentative hypotheses with regard to which dysfunctional neural regions are responsible for movement abnormalities and risk for conversion to psychosis.
In the present study, facial and upper body region movement abnormalities were uniquely associated with symptoms and predicted conversion. Because the orofacial and upper limb regions are predominantly represented in the ventromedial and adjacent areas of the putamen (Walker, 1994), our results point to these regions as prime candidates for neural dysfunction. In contrast, because movement abnormality findings were uniformly nonsignificant for the lower body region, our results suggest that dorsolateral areas of the putamen are not associated with the relationship between movement and psychosis (Walker et al., 1999).
Graybiel (1997) is among those who have suggested that the abnormalities in the basal ganglia may be involved in the generation of dyskinesia and psychotic symptoms. The basal ganglia are linked with forebrain structures that play a role in planning, goal-directed behavior, and monitoring intentions. It has been suggested that irregular neuroactivation, and/or compromised structural or neural circuitry in the basal ganglia, could contribute to psychotic symptomatology. For example, if the striatum plays a role as a goal selector for potential adaptive processes (Schulz et al., 1986), then a disturbance in this subcortical region might result in a “disconnect” between the goals represented in the prefrontal cortex and the basal ganglia's selection of a particular response. In other words, a psychotic patient might experience this disconnection as a feeling that they are not in control of their own behavior or motivations (Neumann & Walker, 2003). Consistent with this hypothesis, Moller and Husby (2000) characterized the initial prodrome of psychosis in first-episode schizophrenia as self-disturbances that were related to losing control of cognitive and affective experiences. Another possibility is that because cortico–striato–pallido–thalamic pathways play a role in linking motor and emotional pathways (Lichter & Cummings, 2001), movement functioning is potentially linked with symptomatology relating to emotion.
The findings reported in the prodromal literature suggest that the diagnostic outcome of at-risk individuals is often either schizophrenia or affective disorders with psychotic components (Cardno, Rijsdijk, Sham, Murray, & McGuffin, 2002; Miller et al., 2002; Riley & Kendler, 2006; Yung et al., 1998). Given this finding, and to remain consistent with the convention in prodromal research, the outcome variable in the present study was conversion to Axis I psychosis (i.e., encompassing outcomes of schizophrenia and affective disorders with psychotic features). Whereas our study found that the conversion group was heterogeneous with both psychotic and affective outcomes, the results of more fine-grained analyses (partitioning the converted group into both schizophrenia and affective disorder with psychotic features outcomes) suggest that movement abnormalities, like many other impairments, are more pronounced in those with schizophrenia outcomes. Although movement abnormalities were elevated in both converted groups in comparison to the nonconverted prodromal adolescents, the schizophrenia group exhibited considerably elevated movement abnormalities in the facial and upper body regions relative to the group with affective disorder with psychotic features. However, because of the small samples that resulted when the groups were divided, any interpretation is tentative. Future studies with larger samples are necessary to determine whether this effect is an artifact of the small sample or reflects a real phenomenon.
Results of the logistic regression analyses provide support for the potential of combining behavioral and physical markers to enhance prediction of psychosis (Cannon et al., 1999). More specifically, movement abnormalities in the limbs and facial area may hold promise for predicting Axis I disorders in youth with prodromal signs. Although research is beginning to support the notion that pharmacological intervention can ameliorate the course of illness—or even prevent the onset of psychosis (Haroun et al., 2006)—the side effects of available medications (e.g., weight gain, diabetes) make providing blanket drug treatment to all prodromal individuals an inappropriate strategy. Movement abnormalities are relatively easy to measure, and thus have potential to serve as risk indicators that, in combination with other behavioral and biological measures, may enhance prediction of psychiatric outcome.
The subgroup of adolescents who were being treated with psychotropic medications presented a methodological challenge as well as a tentatively interesting pattern of preliminary results. Although medications were statistically controlled in each of the present analyses (e.g., group comparisons were conducted with and without covariates), this procedure did not eliminate the potential confound of medication effects. Prescription of psychotropics is expected to target individuals with more severe behavioral dysfunction and, perhaps, concomitant movement abnormalities. Thus, controlling for medication can affect the variance in ratings of symptoms and movements, thereby attenuating the covariance between these two factors. In observing the demographic differences between converted and nonconverted groups, it was noteworthy that a relatively small number of participants treated with antipsychotic medications were in the converted group. Although this observation may indicate a protective effect of medication, such a conclusion is premature. First, there may have been premedication clinical differences between adolescents prescribed and not prescribed this medication (i.e., a selection bias). Second, the biserial correlations conducted in the present study between the different classes of medication and regions of movement were not significant. Finally, the present study was not intended to address this question, and the number of participants prescribed antipsychotic medication in the present sample (n = 8) was too small to draw conclusions about medication effects. As such, future research aimed at examining this question directly is warranted.
Another limitation of the present study concerns the coding procedure which was applied to videos of seated participants and may have masked movement abnormalities in the lower limbs. Future research should examine movement abnormalities using other methodologies; one good candidate methodology is illustrated in a recent study conducted by Schiffman and colleagues (2004) in which movement abnormalities were coded by observing videotapes of playground and social interactions. Finally, future studies should aim to examine demographic differences in outcome groups. In the present study, there were sizable (but nonsignificant) group differences in the ratios of converted individuals who were female and who were African American.
Despite these limitations, the present findings add to the accumulating body of literature indicating that research on prodromal adolescents and young adults holds great promise for identifying the most at-risk individuals prior to the onset of clinical episodes. In addition, this work has the potential to elucidate neural mechanisms that may be linked with the conversion from prodromal status to Axis I psychotic disorders. Both objectives are critical to the long-term goal of preventive intervention.
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Submitted: December 11, 2006 Revised: March 12, 2007 Accepted: March 12, 2007
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Source: Journal of Abnormal Psychology. Vol. 116. (4), Nov, 2007 pp. 796-803)
Accession Number: 2007-17062-012
Digital Object Identifier: 10.1037/0021-843X.116.4.796
Record: 32- Title:
- Multisystemic Therapy for high-risk African American adolescents with asthma: A randomized clinical trial.
- Authors:
- Naar-King, Sylvie. Department of Pediatrics, Wayne State University, Detroit, MI, US, snaarkin@med.wayne.edu
Ellis, Deborah. Department of Pediatrics, Wayne State University, Detroit, MI, US
King, Pamela S.. Department of Pediatrics, Wayne State University, Detroit, MI, US
Lam, Phebe. Department of Pediatrics, Wayne State University, Detroit, MI, US
Cunningham, Phillippe. Department of Psychiatry, Medical University of South Carolina, SC, US
Secord, Elizabeth. Department of Pediatrics, Wayne State University, Detroit, MI, US
Bruzzese, Jean-Marie, ORCID 0000-0002-1866-488X. Department of Child and Adolescent Psychiatry, New York University, NY, US
Templin, Thomas. Department of Pediatrics, Wayne State University, Detroit, MI, US - Address:
- Naar-King, Sylvie, Department of Pediatrics, Wayne State University School of Medicine, Detroit, MI, US, 48201, snaarkin@med.wayne.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 82(3), Jun, 2014. pp. 536-545.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 10
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- adolescents, asthma, health disparities, multisystemic therapy, health outcomes, high risk African Americans
- Abstract:
- Objective: The primary purpose of the study was to determine whether Multisystemic Therapy adapted for health care settings (MST-HC) improved asthma management and health outcomes in high-risk African American adolescents with asthma. Method: Eligibility included self-reported African American ethnicity, ages 12 to 16, moderate to severe asthma, and an inpatient hospitalization or at least 2 emergency department visits for asthma in the last 12 months. Adolescents and their families (N = 170) were randomized to MST-HC or in-home family support. Data were collected at baseline and posttreatment (7 months) based on an asthma management interview, medication adherence phone diary, and lung function biomarker (forced expiratory volume in 1 s [FEV1]). Analyses were conducted using linear mixed modeling for continuous outcomes and generalized linear mixed modeling for binary outcomes. Results: In intent-to-treat analyses, adolescents randomized to MST-HC were more likely to improve on 2 of the measures of medication adherence and FEV1. Per-protocol analysis demonstrated that MST-HC had a medium effect on adherence measures and had a small to medium effect on lung function and the adolescent’s response to asthma exacerbations. Conclusion: There are few interventions that have been shown to successfully improve asthma management in minority youth at highest risk for poor morbidity and mortality. MST, a home-based psychotherapy originally developed to target behavior problems in youth, improved asthma management and lung function compared to a strong comparison condition. Further follow-up is necessary to determine whether MST-HC reduces health care utilization accounting for seasonal variability. A limitation to the study is that a greater number of participants in the control group came from single-parent families than in the MST group. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Asthma; *At Risk Populations; *Blacks; *Treatment Outcomes; *Multisystemic Therapy; Health Disparities
- Medical Subject Headings (MeSH):
- Adolescent; African Americans; Asthma; Ethnic Groups; Female; Hospitalization; Humans; Male; Medication Adherence; Psychotherapy; Young Adult
- PsycINFO Classification:
- Health & Mental Health Treatment & Prevention (3300)
- Population:
- Human
Male
Female - Age Group:
- Childhood (birth-12 yrs)
School Age (6-12 yrs)
Adolescence (13-17 yrs) - Tests & Measures:
- Daily Phone Diary DOI: 10.1037/t05273-000
Family Asthma Management System Scale DOI: 10.1037/t05269-000 - Grant Sponsorship:
- Sponsor: National Institutes of Health
Grant Number: 1R01AA022891-01
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study; Treatment Outcome
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Mar 3, 2014; Accepted: Dec 16, 2013; Revised: Dec 11, 2013; First Submitted: Jul 23, 2013
- Release Date:
- 20140303
- Correction Date:
- 20141124
- Copyright:
- American Psychological Association. 2014
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0036092
- PMID:
- 24588407
- Accession Number:
- 2014-07547-001
- Number of Citations in Source:
- 56
- Persistent link to this record (Permalink):
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-07547-001&site=ehost-live">Multisystemic Therapy for high-risk African American adolescents with asthma: A randomized clinical trial.</A>
- Database:
- PsycINFO
Multisystemic Therapy for High-Risk African American Adolescents With Asthma: A Randomized Clinical Trial
By: Sylvie Naar-King
Department of Pediatrics, Wayne State University;
Deborah Ellis
Department of Pediatrics, Wayne State University
Pamela S. King
Department of Pediatrics, Wayne State University
Phebe Lam
Department of Pediatrics, Wayne State University
Phillippe Cunningham
Department of Psychiatry, Medical University of South Carolina
Elizabeth Secord
Department of Pediatrics, Wayne State University
Jean-Marie Bruzzese
Department of Child and Adolescent Psychiatry, New York University
Thomas Templin
Department of Pediatrics, Wayne State University
Acknowledgement: Phillippe Cunningham is a co-owner of Evidence Based Services. This research was supported by a grant from the National Institute of Health (1R01AA022891-01).
Asthma is the most common cause of hospitalization in children other than infections. Pediatric asthma accounts for more missed school days than any other chronic condition (Centers for Disease Control and Prevention ([CDC], 2011, 2013a, 2013b). Although rates of childhood asthma are increasing worldwide (World Health Organization, 2011), there has been a disproportionate and alarming rise in rates of asthma among urban, disadvantaged, minority children (Agency for Healthcare Research and Quality [AHRQ], 2011; Bloom, Cohen, & Freeman, 2012; CDC, 2011, 2013b). Inner city children, and adolescents in particular, appear to be most at risk for morbidity and mortality as a result of asthma (CDC, 2010; 2013b). Rates of hospitalizations and emergency department (ED) visits for asthma care are also high in this group (AHRQ, 2011; CDC, 2013b).
Poor asthma management is thought to be a major driver of asthma morbidity and mortality (Braman, 2006; Bruzzese et al., 2012; Gustafsson, Watson, Davis, & Rabe, 2006; Rabe et al., 2004). African American children and adolescents appear to be at highest risk for poor adherence to a variety of asthma management tasks (Drotar & Bonner, 2009), including adherence to asthma controller medications (medications prescribed daily to prevent asthma exacerbations) and responding to asthma symptoms (usually with quick relief inhalers; McDaniel & Waldfogel, 2012; Rohan et al., 2010). Therefore, improving illness management may be one way to improve health outcomes among African American adolescents with asthma and reduce asthma-related health disparities.
Social-ecological theory provides a guiding framework (Bronfenbrenner, 1979) for conceptualizing the multiple factors involved in poor illness management (Brown, 2002; Burgess, Sly, & Devadason, 2011; Naar-King, Podolski, Ellis, & Frey, 2006; Yinusa-Nyahkoon, Cohn, Cortes, & Bokhour, 2010). Social-ecological theory posits that complex problem behaviors, such as poor illness management, are multiply determined and reflect difficulties within many systems in which the child and family are embedded. Extrafamilial systems such as school, peers, and community institutions such as the health care system are seen as interconnected with the individual and his or her family and therefore as also affecting illness management.
Because of these multiple factors that affect whether the adolescent is able to adequately manage asthma, educational interventions alone are typically insufficient to improve asthma management and health outcomes (Clark, Griffiths, Keteyian, & Partridge, 2010; Crocker et al., 2011; Kahana, Drotar, & Frazier, 2007). This may be particularly true within urban minority populations of youth with asthma who have high rates of health care utilization such as ED and inpatient visits. Not only are these markers of asthma morbidity, but they are also a major driver of health care costs. More intensive, multicomponent interventions may be necessary to improve asthma management and improve outcomes for such high-risk youth with asthma. Despite this, there are limited studies investigating such multicomponent interventions, and few have exclusively targeted minority adolescents with asthma (AHRQ, 2007; Rhee, Belyea, Hunt, & Brasch, 2011). Only one study to date has targeted minority youth with moderate to severe asthma (requiring daily controller medications). Bruzzese et al. (2011) found that a multisession, school-based intervention with a physician education component improved the percentage of urban minority youth who were prescribed asthma controller medications. However, rates of adherence to these controller medications were not formally assessed. In addition, this study did not focus upon high-risk minority youth with multiple admissions or ED visits.
The purpose of this study was to test whether Multisystemic Therapy (MST; Henggeler, Schoenwald, & Borduin, 2009), a home- and community-based family therapy grounded in the social-ecological model, could improve asthma management (particularly adherence to daily controller medications and responding to asthma exacerbations) and lung functioning in high-risk urban adolescents with moderate to severe persistent asthma. MST was originally developed and empirically validated for the treatment of severe behavior problems in youth and has been successfully adapted as MST-HC (MST Health Care) to target severe problems with illness management in adolescents with chronic medical conditions. In particular, MST-HC has been shown to improve health outcomes in youth with Type 1 diabetes (Ellis et al., 2012), with HIV (Ellis, Naar-King, Cunningham, & Secord, 2006; Letourneau et al., 2013), and with obesity (Naar-King, Ellis, Kolmodin, Cunningham, Jen, et al., 2009). We hypothesized that high-risk African American adolescents with poorly controlled asthma would show greater improvements in asthma management and lung functioning when receiving MST-HC compared to in-home family support (FS).
Method Participants
The study was a randomized trial comparing MST-HC to FS (comparison condition) to improve asthma management in high-risk African American youth. In order to be eligible, adolescents had to be between 12 years 0 months and 16 years 11 months old, to be diagnosed with moderate to severe persistent asthma, to self-identify as African American, and to be residing in a home setting (e.g., not in residential treatment) with a caregiver who was willing to participate in treatment. Having moderate to severe persistent asthma ensured that enrolled participants would be expected to be prescribed a daily asthma controller medication based on national standards of care (National Heart Lung and Blood Institute, National Asthma Education and Prevention Program [NHLBI], 2007). High risk was defined as having at least one asthma-related hospitalization or at least two asthma-related emergency department (ED) visits in the last 12 months at an urban children’s hospital. Exclusion criteria included thought disorder, suicidality, mental retardation, having another chronic health condition, or unable to complete assessments or interventions in English.
Medical record review and direct contact with clinical staff identified 399 patients who were hospitalized or admitted to the ED twice in 1 year for asthma. Of these, 196 were contacted by research staff to further screen for eligibility (see Figure 1 for a consort diagram showing the participant flow through the study). Twelve refused to be screened, and 1 refused to participate in the study after screening (7% refusal rate). One family did not meet eligibility criteria, and 12 families could not be reached between screening and consent. A total of 170 families consented and completed baseline data collection (87% recruitment rate). Eighty-four were randomized to MST-HC and 86 to FS. Two families randomized to FS were removed from the study due to safety concerns that developed during treatment that interfered with the delivery of a home-based intervention, and another family was removed when it was discovered that they did not meet study eligibility criteria. Thus, the final analyzed sample was 167 (84 in MST-HC and 83 in FS). In MST, 85% of families received the allocated intervention (at least 3 sessions; Ellis et al., 2005). In FS, 71% received the allocated intervention.
Figure 1. Consort diagram showing the participant flow through the study. MST-HC = Multisystemic Therapy–Health Care.
Procedure
The study was approved by the university’s internal review board. Participants were initially approached in person by medical staff at the time of a regularly scheduled visit to a university-affiliated pediatric asthma clinic or during an inpatient hospitalization that described the study or were informed of the study by letters sent to their homes. Letters and staff contacts were followed up by phone contacts from study research staff; home-based consent visits were subsequently conducted if families indicated an interest in participating. Baseline data collection, including spirometry, subsequently occurred in the home by trained research assistants. All data collectors were blind to the participant’s study condition. Posttest data collection took place 7 months after baseline data collection. Families were provided $50 to compensate them for participating in each data collection session. Randomization was stratified based on (1) severity of asthma complications as indicated by the number of recent hospitalizations or ED visits (three or more hospitalizations/ED visits in the previous 12 months vs. zero to two hospitalizations/ED visits) and (2) receipt of asthma specialty care (visit to hospital-based multidisciplinary asthma specialty clinic in the last 13 months or not).
Multisystemic Therapy–Health Care (MST-HC)
Adolescents assigned to the intervention condition received MST-HC as adapted for the treatment of poor self-management in youth with asthma (Naar-King, Ellis, Kolmodin, Cunningham, & Secord, 2009). MST includes several key features:
- A comprehensive set of identified risk factors (e.g., across individual, family, peer, school, and neighborhood domains) associated with the problem behavior is targeted through interventions that are individualized for each adolescent.
- These interventions integrate empirically based clinical treatments (e.g., cognitive-behavioral therapy), which historically have been used to focus on a limited aspect of the adolescent’s social ecology (typically only the individual adolescent or at most the adolescent and family), into a broad-based ecological framework that addresses relevant risk factors across family, school, and community contexts.
- Interventions focus on promoting behavioral changes in the adolescent’s natural ecology by empowering caregivers with skills and resources to address difficulties inherent in raising adolescents and empowering adolescents to cope with medical, family, school, and neighborhood problems.
- Services are delivered via a home-based model, which facilitates high engagement and low dropout rate, and are delivered in home, school, and/or neighborhood settings at times convenient to the family.
- MST includes an intensive quality assurance system that aims to optimize youth outcomes by supporting therapist fidelity to MST treatment principles (Henggeler et al., 2006).
Although MST is well specified and operationalized using MST treatment principles, it is not a typical, manualized “one size fits all” intervention in which the therapist follows a set of prearranged tasks in a time-limited sequence. Instead, MST-HC therapists began with an initial multisystemic assessment designed to identify the strengths and weaknesses of the adolescent, the family, and their transactions with extrafamilial systems (e.g., peers, school, community, medical treatment team). A functional assessment of nonadherent behavior through interviews and in-vivo observations was used to identify setting events and the antecedents and consequences of inadequate asthma management across the family, peer, school and community settings. Based upon this assessment, the MST-HC therapist chose from a menu of evidence-based interventions that best treat the identified problem behaviors (e.g., underuse of preventive medications, poor identification of asthma triggers, not carrying rescue medications at all times) and their particular causes in each family. The MST-HC therapist provided treatment to families and their related contacts (extended family members, school personnel, medical team contacts), with the number of sessions per week dependent upon clinical need. MST sessions could take place several times per week (or day) initially and then only weekly once the adolescent’s asthma management had improved. MST-HC treatment goals identified conjointly by family members and the MST-HC therapist during the assessment phase were explicitly targeted for change during the treatment phase. For the proposed study, treatment goals were typically related to illness management (e.g., “takes 90% of controller medications based on medication counts,” “carries inhaler when out of the home”).
MST-HC interventions targeted asthma management problems within the family system, peer network, and the broader community systems within which the family was embedded. MST-HC therapists drew upon a menu of evidence-based intervention techniques that included cognitive-behavioral therapy, behavior therapy, parent training, and behavioral family systems therapy. Individual interventions with adolescents included addressing asthma knowledge deficits or skills deficits such as improper use of inhalers. Family interventions in MST-HC include introducing systematic monitoring, reward, and discipline systems in order to decrease caregiver disengagement from the asthma regimen; developing family organizational routines such as regular controller medication administration times; and helping caregivers to communicate effectively with each other about asthma care and avoidance of triggers. School interventions included helping caregivers improve communication strategies with school personnel such as teachers, counselors, and school nurses regarding their child’s asthma care needs and increasing the accessibility of medications to youth while in school (e.g., ensuring youth could carry their inhaler rather than keeping it in the school office). Interventions within the health care system were also critical and included helping the family resolve barriers to keeping medical appointments and promoting positive family–physician communication and relationships. Families that indicated they had no regular asthma care provider or were interested in making changes in their asthma care were assisted in accessing asthma care as part of the intervention. Based on our previous MST-HC trials, treatment was planned to last for 6 months. Mean length of treatment, excluding dropouts, was 5.14 months (SD = 1.25), and mean number of sessions was 27.09 (SD = 12.03; range = 4–62 sessions). MST-HC was provided by four master’s-level therapists with varied backgrounds (one psychologist, three social workers). Three therapists were African American, and one was White.
Family support (FS)
Families randomized to the comparison condition received weekly, home-based, client-centered, nondirective supportive family counseling. Home-based delivery of services was chosen so as to avoid inequity of treatment dose due to ease of access to services (e.g., home vs. office). The comparison condition was intended to control for improvement due to non-MST specific treatment factors such as positive family expectancies due to entering treatment, receiving positive regard and encouragement for completing asthma care from therapists, and providing family members with opportunities to discuss asthma care. Therefore, the weekly visits had three goals: (1) to provide empathic support to the youth and caregivers regarding the adolescent’s asthma and related care needs, (2) to provide the family with opportunities to discuss barriers they identified to the completion of asthma care, and (3) to discuss the availability of supports to help the family with asthma management. Non-asthma-related problems such as family relationship problems could also be discussed during the visits if requested by the family. Therapists accomplished these goals by providing Rogerian, client-centered, nondirective counseling (Rogers, 1951). This counseling emphasizes empathic and reflective listening in order to facilitate growth that stems from within the individual. In order to provide support in the areas that were most difficult for the family, therapists began each session by asking open-ended questions regarding asthma management during the prior week. Youth completed a checklist of their asthma symptoms (if any) during the prior week to guide the conversation. Family members were then verbally reinforced for what was going well; when barriers to care were raised, the therapist did not address these concerns with skills-building or problem-solving interventions but rather supported the family to come up with their own ideas regarding ways to address such challenges.
The FS intervention was 6 months in length and hence was matched to MST-HC for length of treatment. Since MST session dose is flexible, matching the control condition for dose was not possible. However, an approximate dose of weekly 45-min sessions consistent with traditional outpatient therapy approaches (and therefore with what would be provided in a real-world setting) was chosen. Mean length of treatment, excluding dropouts, was 4.20 months (SD = 1.78), and mean number of sessions was 11.03 (SD = 5.74; range = 3–24). FS was provided by six master’s-level clinicians and one bachelor’s-level clinician. Five therapists were African American, and two were White.
Treatment fidelity
In order to promote fidelity to the MST-HC model, state-of-the-art quality assurance protocols were used that included an initial 5-day training, weekly on-site clinical supervision from a PhD-level supervisor with an extensive background in MST-HC, weekly phone consultation with an MST expert with experience with the application of MST to chronic health conditions, and quarterly booster trainings. The initial standard MST 5-day orientation training was adapted by the research team to include formal asthma education for MST therapists as well as education regarding factors that are predictive of poor treatment adherence and symptom exacerbations among adolescents with asthma. MST therapists were trained to have sufficient knowledge regarding asthma to enable them to conduct asthma adherence interventions with families (e.g., methods of environmental control, differences between use of rescue and controller medications, using asthma action plans for symptom management). Quality assurance protocols also included use of a manual on how to treat MST-HC with youth with high-risk asthma developed during a feasibility trial (Naar-King, Ellis, Kolmodin, Cunningham, & Secord, 2009) and feedback on therapist and supervisor fidelity to MST procedures (Henggeler & Schoenwald, 1998; Schoenwald, 1998). All sessions were audio-recorded, and independent coders rated one randomly selected session per month per therapist using the MST code scheme (Huey, 2001) adapted for MST-HC (Ellis, Naar-King, Templin, Frey, & Cunningham, 2007).
For the FS condition, quality assurance protocols included a detailed manual, an initial 3-day training, and a minimum of biweekly on-site clinical supervision from a PhD-level supervisor with experience with Rogerian psychotherapy. All sessions were audio-recorded, and supervisors reviewed one tape per month. To ensure that elements of MST-HC were not present in the comparison condition, 15 FS tapes (approximately one per quarter during the active intervention phase) were randomly selected and coded by trained MST coders who were blind to treatment condition.
Measures
Asthma management
The Family Asthma Management System Scale (FAMSS; McQuaid, Walders, Kopel, Fritz, & Klinnert, 2005) is a clinical interview completed conjointly with caregivers and teens. Questions are open–ended, and the interviewer must resolve any discrepancies between the reporters by making standardized judgments regarding degree of asthma management on a 9-point scale (1 = poor management, 9 = excellent management). The interview takes approximately 45 min to complete. The measure has been found to be correlated with objective measures of asthma management such as electronic monitors and accounted for a significant percentage of variance in asthma morbidity in a sample of youth ages 7 to 17 (McQuaid et al., 2005), and it has demonstrated validity and sensitivity to intervention effects in low-income children (Celano, Holsey, & Kobrynski, 2012; Celano, Klinnert, Holsey, & McQuaid, 2011). Scale developers trained study raters, and interrater reliability between raters and scale developers was high (intraclass correlations = .933). Three illness management–specific subscales—Medication Adherence, Child Response to Symptoms and Exacerbations, and Family Response to Symptoms and Exacerbations—were used in the present study, and they have been shown to be related to objective measures of medication adherence and asthma morbidity (McQuaid et al., 2005).
Adherence to daily corticosteroid medication (i.e., controller medication) was also assessed with the Daily Phone Diary (DPD; Modi & Quittner, 2006), a cued recall procedure that collects information about participants’ activities, companions, and moods during the previous 24 hr. Adolescents completed two DPD assessments within a 2-week period. For all activities lasting 5 min or more, participants reported the type of activity, duration, and who was present. Interviewers assisted participants in reconstructing their day as accurately as possible by providing prompts such as time of day or information about the previous activity (e.g., “After you finished dinner, what did you do next?”). For the current study, information from the two DPD assessments was combined to determine use of controller medication in either of the 24-hr periods (1 = participant took controller medication on at least one of the 2 days, 0 = participant did not take controller medication on either day).
Lung functioning
Pulmonary functioning was assessed by using forced expiratory maneuvers obtained with a portable calibrated recording spirometer (KoKo) at the time of the research interview. Forced expiratory volume in 1 s (FEV1) provides reliable and reproducible information about airflow in health and disease and has been found to correlate with clinical outcomes (Knudson, Kaltenborn, Knudson, & Burrows, 1987). While standing, the subject was encouraged to perform between five and eight maneuvers to obtain three acceptable tracings in order to measure the FEV1. All research assistants were trained by the KoKo spirometry’s training specialist. Then the asthma specialty clinic’s respiratory therapist evaluated all research assistants performing spirometry measurements based on American Thoracic Society (ATS) standards.
Statistical Analyses
Using t tests for continuous variables and chi-square tests for categorical variables, demographic and other baseline variables were compared between the treatment and control groups, as well as between those who completed the 7-month follow-up assessment and those who did not. The primary outcomes at the follow-up were analyzed for both the intent-to-treat sample (all randomized participants) and per-protocol sample (participants who received a predefined minimum dose of treatment). Methodologists have increasingly argued that adopting intent-to-treat approaches as the sole analytical strategy can ignore valuable information available in the other strata of participants (Amico, 2009). Instead, a profile approach for outcomes addresses the impact of offering the intervention program within a given existing service (intent-to-treat) as well as the impact of the intervention when received. Thus, intent-to-treat analyses included all participants who were randomized into either the MST-HC group (n = 84) or the FS group (n = 83) and not removed during the trial, and per-protocol analyses included participants in MST-HC (n = 71) and FS (n = 59) groups who received the minimum treatment dosage.
Analyses were conducted using linear mixed modeling for continuous outcomes (SAS PROC MIXED) and generalized linear mixed modeling for binary outcomes (i.e., DPD; PROC GLIMMMIX). Each of the mixed models controlled for gender, age at baseline, family income, number of treatment sessions, and single-parent household and examined the effects of time, treatment group, and the Time × Treatment Group interaction. Models predicting FEV1 also controlled for adolescents’ height. Missing data at follow-up were accounted for in the mixed effects models using maximum-likelihood estimation (13.25% in MST, 2.38% in control).
Results Study Retention
At follow-up, 92.22% of the sample (n = 154) completed assessments. Participants in FS were more likely than those in the MST group to have missed the 7-month follow-up (χ2 = 6.87, p < .01). Participants who completed the follow-up assessment did not differ from noncompleters on baseline demographics, asthma management measures, or FEV1 (an indicator of lung functioning; p > .05).
Baseline Data
Descriptive statistics for the MST-HC and FS groups are presented in Table 1. Adolescents in the FS group were more likely than those in the MST-HC group to live in single-parent households (χ2 = 4.66, p = .03).
Baseline Characteristics of Adolescents Randomized to MST and Control Groups
Intent-to-Treat Analyses
As shown in Figures 2, 3, and 4, results of intent-to-treat analyses indicated significant Time × Treatment interactions for FEV1 (b = .02 [95% CI .0004, .04], SE = .01, p < .05), FAMSS Medication Adherence (b = .14 [95% CI .02, .26], SE = .06, p = .03), and DPD Medication Adherence (b = .18 [95% CI .02, .34], SE = .08, p = .03), with the MST-HC group demonstrating greater improvement in lung function and controller medication adherence than the FS group. There were no differences in child or family response to symptoms. As seen in Table 2, treatment had a medium effect on changes in FAMSS and DPD adherence to medication and a small effect on changes in FEV1.
Figure 2. Difference between the Multisystemic Therapy (MST) and control groups in change in lung function (FEV1) from baseline to the 7-month follow-up. FEV1 = forced expiratory volume in 1 s.
Figure 3. Difference between Multisystemic Therapy (MST) and control groups in change in adherence to controller medication from baseline to the 7-month follow-up, as reported on the Family Asthma Management System Scale (FAMSS).
Figure 4. Difference between Multisystemic Therapy (MST) and control groups in change in the proportion of participants reporting adherence to controller medication on the Daily Phone Diary (DPD) from baseline to the 7-month follow-up.
Mean Change in Continuous Outcome Variables From Baseline to 6 Months for MST and Control Groups, and Magnitude (Effect Size) of Differences in Change Scores Between Groups
Per-Protocol Analyses
The per-protocol analyses indicated that, unlike the intent-to-treat analyses, youth receiving at least a minimum dose of MST-HC had significantly greater improvements in Child Response to Symptoms and Exacerbations (b = .11 [95% CI .01, .21], SE = .05, p = .04). Similar to the intent-to treat analyses, participants receiving MST-HC also had greater improvement in lung function (FEV1; b = .03 [95% CI .01, .05], SE = .01, p = .01) and adherence to controller medication, as measured by the FAMSS (b = .15 [95% CI .03, .27], SE = .06, p = .02) and by the DPD (b = .21 [95% CI .03, .39], SE = .09, p = .03). As seen in Table 2, the size of the effect of MST-HC was small to medium on changes in lung function and medium on medication adherence when the treatment sample was considered.
DiscussionThere are few evidence-based interventions to improve asthma management in minority adolescents who are at high risk for morbidity and mortality. Results demonstrate that MST-HC, already shown to be effective in improving medication adherence in youth with diabetes and HIV, successfully improved asthma medication adherence and lung function in this population in an intent-to-treat analysis with a strong comparison condition, home-based supportive family counseling. Furthermore, MST-HC improved adolescents’ response to asthma symptoms and exacerbations and had a small to medium effect on lung function (using an objective health outcome) among youth who received the allocated treatment. Given that FEV1 may take some time to improve following increasing use of daily controller medications (Strunk, 2007), this finding immediately posttreatment is promising and suggests the possibility of further improvement with longer follow-up. With each 10% increase in FEV1, there is a progressive decrease in asthma attacks (Fuhlbrigge et al., 2001), thus decreasing health care utilization. We intend to assess the impact of MST-HC on health care utilization over 12 months of follow-up, as it is widely recognized that there are seasonal variations in asthma exacerbations and therefore on rates of hospital or ED visits (NHLBI, 2007).
This study documented the efficacy of MST-HC for youth with asthma compared to a rigorous attention control matched for multiple nonspecific treatment effects (e.g., family interactions, home-based, monitoring of symptoms). To date, other MST-HC interventions have utilized standard care controls (Ellis et al., 2008) or comparison treatments provided only to the adolescent (Ellis et al., 2012; Letourneau et al., 2013). The present data suggest that there are crucial aspects of MST-HC that are responsible for outcomes, including the provision of evidence-based family interventions and that simple attention, warmth, and support to the caregiver of the youth does not account for the findings. Furthermore, MST, which is present-focused and action-oriented, may be more feasible and acceptable to minority families than simply creating opportunities for families to interact around asthma, as evidenced by increased treatment retention and dose received in the MST-HC group compared to FS.
The dose of MST-HC in the present study was lower than in our prior studies with youth with poorly controlled diabetes (Ellis et al., 2005; Ellis et al., 2012). It is possible that families did not perceive asthma, a common childhood illness, to be as severe as diabetes and therefore did not participate in as many sessions. Both the MST and FS groups received about half the expected dose of treatment. It is possible that to meet the needs of high-risk families, or multistressed families, twice the number of expected sessions must be offered to account for frequent cancellations and rescheduling. Alternatively, a lower dose of treatment may have been sufficient to improve adherence to asthma medication, relative to our work with other populations such as diabetes, where multiple illness management behaviors (insulin adherence, blood glucose testing, dietary management) were targeted. Future research should test which high-risk families may benefit from a lower dose of treatment and which families require more intensive services. While differential attrition may be a limitation of the study, both study arms achieved high levels of study retention that exceeded published guidelines for acceptability (CDC, 2007; Lyles et al., 2007; Valentine & Cooper, 2008). Another limitation to the study is that a greater number of participants in the control group came from single-parent families than in the MST group.
Additional research is also necessary to support the transportability of MST-HC to community settings. Economic analysis is necessary to determine cost offsets in terms of reduced health care utilization both during the trial and in the future. Future studies can test whether utilizing paraprofessional staff can maintain efficacy and reduce costs. In one version of MST-HC for youth with HIV in rural settings, paraprofessionals were utilized to augment services provided by master’s-level clinicians (Letourneau et al., 2013). The Centers for Disease Control has called for expanded use of community health workers in services for chronic disease (Nichols, Ussery-Hall, Griffin-Blake, & Easton, 2012), with careful attention to implementation and training (Lewin et al., 2005). Implementation science is the scientific study of methods to promote the uptake of research findings and evidence-based practice to improve the quality and effectiveness of health care (Eccles et al., 2009). Implementation science studies of MST in mental health are under way (e.g., Glisson et al., 2010; Ogden et al., 2012), and this information can guide similar work in health care, particularly for high-risk minority youth. This work will not only address the science–practice gap but also has the potential to reduce health disparities in chronic illness outcomes.
In summary, there are few interventions that have been shown to successfully improve asthma management in minority youth at highest risk for poor morbidity and mortality. MST, a home-based psychotherapy originally developed to target behavior problems in youth, improved adherence to daily controller medications and lung function immediately posttreatment. Further follow-up is necessary to determine whether MST-HC reduces health care utilization accounting for seasonal variability.
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Submitted: July 23, 2013 Revised: December 11, 2013 Accepted: December 16, 2013
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Source: Journal of Consulting and Clinical Psychology. Vol. 82. (3), Jun, 2014 pp. 536-545)
Accession Number: 2014-07547-001
Digital Object Identifier: 10.1037/a0036092
Record: 33- Title:
- Nonsuicidal self-injury among 'privileged' youths: Longitudinal and cross-sectional approaches to developmental process.
- Authors:
- Yates, Tuppett M.. University of California, Department of Psychology, Riverside, CA, US, Tuppett.Yates@ucr.edu
Tracy, Allison J.. Centers for Research on Women, Wellesley College, Wellesley, MA, US
Luthar, Suniya S.. Developmental and Clinical Psychology Programs, Teachers College, Columbia University, New York, NY, US - Address:
- Yates, Tuppett M., University of California, Department of Psychology, 2320 Olmsted Hall, Riverside, CA, US, 92521, Tuppett.Yates@ucr.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 76(1), Feb, 2008. Suicide and Nonsuicidal Self-Injury. pp. 52-62.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- nonsuicidal self-injury, privileged youths, developmental psychopathology, delinquency, zero-inflated Poisson regression models
- Abstract:
- This investigation examined process-level pathways to nonsuicidal self-injury (NSSI; e.g., self-cutting, -burning, -hitting) in 2 cohorts of suburban, upper-middle-class youths: a cross-sectional sample of 9th-12th graders (n = 1,036, 51.9% girls) on the West Coast and a longitudinal sample followed annually from the 6th through 12th grades (n = 245, 53.1% girls) on the East Coast. High rates of NSSI were found in both the cross-sectional (37.2%) and the longitudinal (26.1%) samples. Zero-inflated Poisson regression models estimated process-level pathways from perceived parental criticism to NSSI via youth-reported alienation toward parents. Pathways toward the initiation of NSSI were distinct from those accounting for its frequency. Parental criticism was associated with increased NSSI, and youth alienation toward parents emerged as a relevant process underlying this pathway, particularly for boys. The specificity of these pathways was explored by examining separate trajectories toward delinquent outcomes. The findings illustrate the prominence of NSSI among 'privileged' youths, the salience of the caregiving environment in NSSI, the importance of parental alienation in explaining these relations, and the value of incorporating multiple systems in treatment approaches for adolescents who self-injure. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Human Development; *Juvenile Delinquency; *Psychopathology; *Self-Inflicted Wounds; *Upper Class
- Medical Subject Headings (MeSH):
- Adolescent; Cohort Studies; Conflict (Psychology); Cross-Sectional Studies; Female; Humans; Juvenile Delinquency; Longitudinal Studies; Male; Parent-Child Relations; Personality Inventory; Poisson Distribution; Probability; Risk Factors; Self-Injurious Behavior; Sex Factors; Social Alienation; Social Class; Substance-Related Disorders; Suicide, Attempted
- PsycINFO Classification:
- Behavior Disorders & Antisocial Behavior (3230)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Childhood (birth-12 yrs)
School Age (6-12 yrs)
Adolescence (13-17 yrs) - Tests & Measures:
- Inventory of Parent and Peer Attachment-Alienation subscale
Functional Assessment of Self-Mutilation
Child Behavior Checklist Youth Self-Report (YSR) form--Rule-Breaking subscale
Multidimensional Perfectionism Scale - Grant Sponsorship:
- Sponsor: National Institute of Mental Health
Grant Number: R01-DA14385
Recipients: No recipient indicated
Sponsor: William T. Grant Foundation
Recipients: No recipient indicated - Methodology:
- Empirical Study; Longitudinal Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Aug 20, 2007; Revised: Aug 10, 2007; First Submitted: Feb 9, 2007
- Release Date:
- 20080128
- Correction Date:
- 20120827
- Copyright:
- American Psychological Association. 2008
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/0022-006X.76.1.52
- PMID:
- 18229983
- Accession Number:
- 2008-00950-008
- Number of Citations in Source:
- 53
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2008-00950-008&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2008-00950-008&site=ehost-live">Nonsuicidal self-injury among 'privileged' youths: Longitudinal and cross-sectional approaches to developmental process.</A>
- Database:
- PsycINFO
Nonsuicidal Self-Injury Among “Privileged” Youths: Longitudinal and Cross-Sectional Approaches to Developmental Process
By: Tuppett M. Yates
Department of Psychology, University of California, Riverside;
Allison J. Tracy
Centers for Research on Women, Wellesley College
Suniya S. Luthar
Developmental and Clinical Psychology Programs, Teachers College, Columbia University
Acknowledgement: Preparation of this article was funded in part by National Institute of Mental Health Grant R01-DA14385 and by the William T. Grant Foundation. We thank Monica Ghailian and Chandra Reynolds for their assistance and comments.
In recent years, nonsuicidal self-injury (NSSI; e.g., self-cutting, -burning, -hitting) has transcended the bounds of clinical wards and medical journals to reveal itself as a prominent and burgeoning health concern among community youths (Gratz, Conrad, & Roemer, 2002; Laye-Gindhu & Schonert-Reichl, 2005; Muehlenkamp & Guttierez, 2004; Ross & Heath, 2002; Whitlock, Eckenrode, & Silverman, 2006). However, the extant literature on NSSI, particularly in community settings, has focused on descriptive studies to the relative neglect of theoretically informed, process-oriented investigations that recognize NSSI as both a developmental and clinical phenomenon. Addressing this gap in the literature, the present study examined putative developmental processes underlying self-injurious pathways in two cohorts of suburban, upper-middle-class youths: a cross-sectional sample of 9th–12th graders on the West Coast and a longitudinal sample that was followed annually from the 6th through 12th grades on the East Coast.
The Phenomenology of NSSIBuilding on previous definitions of NSSI (see Simeon & Favazza, 2001, for review), this study examined self-inflicted, direct, socially unacceptable destruction or alteration of body tissue that occurred in the absence of conscious suicidal intent or pervasive developmental disorder. Recent community studies point to striking rates of NSSI, as defined here, among adolescents. For example, Gratz et al. (2002) found that 38% of a college student sample endorsed a history of NSSI, whereas Ross and Heath (2002) found that 14% of a high school sample reported NSSI (see also Laye-Gindhu & Schonert-Reichl, 2005). Drawing on a large, multisite study of more than 3,000 college students, Whitlock et al. (2006) found that 17% of college students reported NSSI and that 75% of these self-injurers endorsed more than one episode.
The prevalence and phenomenology of NSSI across different gender, ethnic, and economic groups remain unclear. Although some studies have suggested that girls are 1.5–3 times more likely to self-injure than are boys (Clery, 2000; Favazza, 1999), others have suggested that gender differences are less pronounced (Garrison et al., 1993; Gratz et al., 2002; Tyler, Whitbeck, Hoyt, & Johnson, 2003). In contrast to gender differences, socioeconomic and ethnic differences have rarely been examined in studies of NSSI. A recent survey of college students found no relation between social class (as indicated by parental education level) and NSSI (Whitlock et al., 2006), but other findings have suggested that rates of self-injury may be elevated among low-income individuals (Nada-Raja, Skegg, Langley, Morrison, & Sowerby, 2004). Similarly, although a few studies have reported elevated rates of NSSI among Caucasian individuals (e.g., Ross & Heath, 2002), others have revealed significant rates among minority youths (Gratz, 2006; Lipschitz et al., 1999; Marshall & Yazdani, 1999; Nada-Raja et al., 2004). Building on this literature, the present study examined the phenomenology and sociodemographic patterning of NSSI among 1,300 high school students who were attending suburban coeducational schools that primarily cater to children of highly educated, white-collar professionals.
Developmental Pathways to NSSIRelative to the descriptive literature on NSSI, less is known about developmental pathways toward self-injurious outcomes. Retrospective findings strongly implicate the quality of the caregiving environment in the etiology of NSSI, with up to 79% of adult self-injurers reporting a childhood history of abuse or neglect (Gratz et al., 2002; Low, Jones, MacLeod, Power, & Duggan, 2000; van der Kolk, Perry, & Herman, 1991; Wiederman, Sansone, & Sansone, 1999). However, little is known about etiologic and developmental processes underlying NSSI in adolescence, despite evidence that this is the period during which self-injurious pathways are typically initiated (Favazza, 1999). Moreover, researchers have rarely examined the potential contribution of less extreme forms of negative parent–child interactions (e.g., critical parenting) to NSSI (see Wedig & Nock, 2007). Building on a recent application of a developmental psychopathology perspective on NSSI (Yates, 2004), this study examined developmental pathways and mechanisms by which parental criticism may contribute to NSSI in adolescence.
Grounded in an understanding of normative development and informed by core tenets of attachment and organizational theories of development (Sroufe, 1990), Yates (2004) identified several process-level pathways toward NSSI that may follow from the deleterious impact of adverse caregiving on development. In this view, harsh or critical parenting may contribute to NSSI by undermining emerging representations of relationships as reliable and rewarding (i.e., motivational processes); complementary views of the self as worthy of care (i.e., attitudinal processes); capacities to integrate experience across multiple levels of thinking and feeling (i.e., integrative processes); abilities to modulate emotion and arousal (i.e., emotional processes); and/or resources to form reciprocal and empathic relationships (i.e., relational processes). This investigation tested a motivational pathway toward NSSI, wherein we hypothesized that parental criticism would undermine adolescents' representations of others, thereby prompting them to turn toward the self and the body, rather than to others, in times of challenge or distress. This motivational hypothesis is consistent with evidence that parental criticism is associated with invalidating and rejecting caregiving environments (McCarty, Lau, Valeri, & Weisz, 2004), which may instill a sense of alienation from caregivers and a broader mistrust of others (Fonagy, Target, & Gergely, 2000; Sroufe, 1990), as well as with the overwhelming evidence that NSSI subserves self- and affect-regulatory functions (Brain, Haines, & Williams, 1998; Nock & Prinstein, 2004, 2005).
Developmental Specificity of Self-Injurious PathwaysAlthough recent studies have considered self-injurious pathways and relevant developmental processes theoretically (Yates, 2004) and empirically (Ross & Heath, 2003; Yates, Carlson, & Egeland, in press), there remains a pressing need to ascertain whether identified risks and processes provide explanatory power that is unique to self-injurious outcomes or whether they are merely characteristic of global psychopathology. Contrary to the hypothesis that a sense of alienation from others will prompt individuals to turn in and against the self in times of duress or need, an alternative model predicts that adolescents may turn out and against others as a consequence of negative relational representations (Egeland, Yates, Appleyard, & van Dulmen, 2002; Sankey & Huon, 1999). Thus, our final aim in this investigation was to explore whether the motivational vulnerabilities that follow from critical parenting (i.e., youth alienation toward parents) contributed to delinquent outcomes in adolescence (i.e., rule-breaking behavior) and whether these paths differ between girls and boys and/or from those toward NSSI.
SummaryThis study evaluated theoretically informed, process-level pathways between perceived parental criticism and NSSI among “privileged” youths in a cross-sectional sample of 9th–12th graders and a longitudinal sample that was followed from the 6th through 12th grades. Our first aim in this study was to describe the phenomenology of NSSI among children of highly educated, white-collar professionals, a population that has been largely overlooked in previous studies of psychopathology (see Luthar, 2003, for discussion). Second, we sought to evaluate a motivational pathway to NSSI, in which we predicted that critical parenting would contribute to NSSI via its negative impact on parental representations, as reflected by increased feelings of alienation toward parents. Given prior evidence of meaningful gender differences in NSSI, these processes were estimated independently for girls and for boys. Our final goal was to explore the specificity of the proposed motivational pathway toward NSSI by examining a parallel model using delinquent behavior as the outcome. Together, these goals draw on the complementary strengths of cross-sectional and longitudinal research designs to enable the description and preliminary temporal specification of self-injurious pathways among suburban, upper-middle-class youths.
Method Participants
West Coast cross-sectional sample
Participants in this sample were drawn from a single high school in a West Coast suburban community. As of the 2000 census, the median household income in this community was $91,904 (equivalent to ~$111,116 in 2006); 69.1% of adults had at least a college degree, and only 1.9% of families lived at or below the poverty line. Of the original 1,185 participants, 1,036 (538 girls, 498 boys) provided complete data on NSSI. The current sample was evenly distributed across the 9th, 10th, 11th, and 12th grades. The ethnic composition of the sample was 70.7% Caucasian, 18.1% Asian, 2.4% Hispanic, 1.5% Black, 1% other minority (e.g., Native American), and 6.3% multiracial. Students who provided complete data on NSSI did not differ from the larger sample with respect to salient demographics, including ethnicity, gender, and grade membership. Participants who provided complete data on NSSI but not on other relevant variables (e.g., parental criticism) were not included in the path analyses (n = 57, 5.5%). The ethnic, gender, and grade distribution of the sample in the path analyses was comparable to that for the broader sample.
Students in the West Coast sample were assessed at the request of the local community and school. Following a series of incidents involving substance use and suicide attempts, community representatives invited Suniya S. Luthar to present available data on youths in such communities and to discuss possibilities for the assessment of students to ascertain intervention needs. Prior to data collection, the entire student body in both schools saw a videotaped presentation by Suniya S. Luthar that introduced the study, briefly explained that little was known about the lives of children of well-educated professionals, requested participation while clarifying that it was in no way required, and assured the anonymity of responses. Parents were sent letters that explained the study and gave them the opportunity to refuse consent for their child to participate. All 1,185 students who were in school (243 students were absent) and were eligible to participate (8 students were in special education) on the day of data collection completed the questionnaires, yielding an 82.9% response rate. Data collection occurred in the classrooms via paper-and-pen survey; there was no collection of personally identifying information. The administration of measures was performed by community personnel and teachers, who were instructed simply to maintain order (i.e., not to walk around the room and potentially glimpse students' responses). Upon completing the questionnaire, students sealed their response packets in an envelope and received a gift certificate in appreciation for their participation. All procedures were reviewed and approved by the Institutional Review Board for the Protection of Human Subjects, Teachers College, Columbia University.
East Coast longitudinal sample
Participants in this sample were drawn from the New England Study of Suburban Youth (NESSY), which is a longitudinal study of development and adaptation among a cohort of high-income, suburban schoolchildren first recruited in the 6th grade and followed annually thereafter through the 12th grade (Luthar & Goldstein, in press; Luthar & Latendresse, 2005; Luthar, Shoum, & Brown, 2006). The original NESSY sample consisted of 314 sixth graders (150 girls, 164 boys) from the two schools in this upper-middle-class community of highly educated, white-collar professionals. As of the 2000 census, the median household income in this community was $125,381; 32.8% of the adults had earned a graduate degree, and only 3% of the students received free or reduced-price lunches (Luthar & Sexton, 2004). At the time of the 12th-grade assessment, when NSSI was assessed, all 245 students (130 girls, 115 boys) who were in school (48 students were absent) and were eligible to participate (17 students did not have parental consent) completed the questionnaires, yielding a 79.5% response rate. The sample was 89% Caucasian and 5% Hispanic; the remaining 6% of the sample was evenly distributed across Asian, African American, and other racial groups, including multiracial identifications. Relative to the original sample, there were no significant differences in the ethnic or gender makeup of the 12th-grade sample, though the current sample was slightly more diverse than the original sample, which was 93% Caucasian. Participants who provided complete data in Grade 12 but who were not assessed at earlier time points were not included in the path analyses, because they were missing data on key predictor variables (e.g., parental criticism: n = 34, 13.9%). The ethnic and gender distribution of the sample in the path analyses was comparable with that for the broader sample.
As in the West Coast sample, the NESSY grew out of community concern about the welfare of children, which precipitated a school-based initiative to understand and encourage positive youth development. Student recruitment was based on passive consent procedures. Administrators sent letters to parents that described the study, emphasized that data would be presented only in aggregate form, and requested notification from parents who did not wish their child to participate. A few days prior to data collection, the parents were again informed about the study and given the opportunity to request that their child not participate. The children themselves were given the opportunity to decline to participate in the study. Data were collected in the classrooms. Test items were administered both visually and orally to prevent bias due to variability in reading abilities. Upon completion of each data collection, gift certificates were provided to all participating students. All procedures were reviewed and approved by the Institutional Review Board for the Protection of Human Subjects.
Measures
Parental criticism
Parental criticism was measured with the Multidimensional Perfectionism Scale (MPS; Frost, Marten, Lahart, & Rosenblate, 1990). The MPS consists of 35 statements that describe a range of perfectionistic beliefs, which are rated with a 5-point Likert scale from 1 (strongly disagree) to 5 (strongly agree). The Parental Criticism subscale consists of 4 items, including “I am punished for doing things less than perfectly,” “My parents never try to understand my mistakes,” “I never feel like I can meet my parents' expectations,” and “I never feel like I can meet my parents' standards.” Parental criticism was assessed cross-sectionally in the West Coast sample (αs = .77–.85) and was averaged across Grades 6, 7, and 8 in the East Coast sample (αs = .76–.86).
Parental alienation
Adolescents' feelings of alienation toward their parents were assessed with the Alienation subscale of the Inventory of Parent and Peer Attachment (IPPA; Armsden & Greenberg, 1987). The IPPA consists of 50 items (25 pertaining to each parent), which are rated on a 5-point Likert scale from 1 (almost never or never true) to 5 (almost always or always true). The Alienation scale consists of 12 items (6 for each parent) that assess the youth's feelings of anger, isolation, and mistrust in relating to each parent (e.g., “Talking over my problems with my mother/father makes me feel ashamed or foolish,” “I feel angry with my mother/father”). Due to the high correlations between maternal and paternal alienation (rs = .67–.71), we averaged these scales to create a global alienation score. Parental alienation was assessed cross-sectionally in the West Coast sample (αs = .86–.88) and was averaged across Grades 9, 10, and 11 in the East Coast sample (αs = .76–.85).
NSSI
We used the Functional Assessment of Self-Mutilation (FASM; Lloyd, Kelley, & Hope, 1997) to assess rates and methods of NSSI during the 12 months preceding the time of data collection. The utility of the FASM has been established across several studies (Guertin, Lloyd-Richardson, Spirito, Donaldson, & Boergers, 2001; Nock & Prinstein, 2004, 2005). Respondents indicated whether and how often they had engaged in 11 different forms of NSSI, including cutting or carving skin, picking at a wound, self-hitting, scraping skin to bleed, self-biting, picking areas of body to bleed, inserting objects under skin or nails, self-tattooing, burning skin, pulling out hair, or erasing skin to bleed. Frequency was rated using a 5-point scale that ranged across 1 (0 times), 2 (1 time), 3 (2–5 times), 4 (6–10 times), and 5 (≥ 11 times). NSSI was assessed cross-sectionally in the West Coast sample (αs = .84–.91) and in the 12th grade in the East Coast sample (αs = .67–.85).
Delinquent behavior
Delinquent behavior was assessed with the Rule-Breaking subscale of the Youth Self-Report (YSR) form of the Child Behavior Checklist (Achenbach, 1991b). This measure consists of 118 behavioral items rated by the adolescent on a 3-point scale as 0 (not true), 1 (somewhat or sometimes true), or 2 (very true or often true). T scores on the YSR stem from extensive normative data, evidence short-term test–retest reliability, and discriminate between clinic-referred and nonreferred youths (Achenbach, 1991a). The Rule-Breaking subscale includes items that capture a range of delinquent behaviors, such as associating with deviant peers, lying, and stealing. Delinquent behavior was assessed cross-sectionally in the West Coast sample (αs = .71–.76) and in the 12th grade in the East Coast sample (αs = .83).
Statistical Analyses
As is often observed in community-based studies of psychopathology, NSSI was not normally distributed across participants in this investigation. In both samples, the distribution of NSSI was positively skewed with a precipitous drop, such that even a transformed distribution would substantially violate the assumptions of normality required for parametric analytic approaches (Papoulis & Pillai, 2002). This characteristic inherent in the data requires a special case of regression analysis called zero-inflated Poisson (ZIP) regression. ZIP models are well suited to the analysis of count data with excess zeros (Lambert, 1992). The present analyses employed ZIP path models to permit the simultaneous prediction of two variables that, together, describe the obtained distribution of NSSI: namely, the occurrence of NSSI (i.e., “0” representing noninjurers, “1” representing all NSSI values greater than zero) and the frequency of NSSI once initiated (i.e., the specific value of NSSI greater than zero).
While ZIP regression models appropriately account for the distinct nonnormality of NSSI, several characteristics of this analytic paradigm warrant consideration. First, the statistical power needed for detection of a given effect size is greater than in the standard linear regression paradigm (Dufour & Zung, 2005). Second, standardized model fit indices and estimates of effect sizes (e.g., R2, standardized regression weights) developed for linear regression analysis are not available (Muthén & Muthén, 1998–2006). Third, the estimation technique required for appropriate handling of missing data in a Poisson-distributed dependent variable requires Monte Carlo numerical integration, which precludes the estimation of the statistical significance of indirect pathways (Muthén & Muthén, 1998–2006). Therefore, we reported unstandardized parameter estimates and their standard errors and constructed 95% confidence intervals to compare parameters across groups.
The path models were conducted in a general latent-variable modeling framework with multiple groups, which allowed the simultaneous estimation of hypothesized pathways across gender. Initial models specified only the direct relation between perceived parental criticism and NSSI. Next, the hypothesized mediating pathway from parental criticism through alienation to NSSI was introduced. As discussed previously, we also estimated these pathways in the prediction of delinquent behavior to test the specificity of the predicted motivational path for NSSI. In all models, the data from the West and East Coast samples were fit separately. Because the sample size was considerably smaller in the East Coast sample, resulting in relatively low statistical power, we have presented the results of the East Coast models as preliminary evidence of the directionality of the hypothesized processes.
Results Descriptive Analyses
Table 1 details the frequency of NSSI methods in each sample for girls and for boys during the preceding year. The FASM item corresponding to “pick at a wound” was not included in these analyses, because disproportionately high rates of endorsement suggested that this item captured a largely normative adolescent behavior. Across the remaining forms of NSSI, West Coast participants endorsed higher levels of NSSI (7.7% reported one incident, 29.5% reported more than one incident) than did East Coast respondents (10.2% reported one incident, 15.9% reported more than one incident), χ2(2, N = 1,281) = 18.68, p < .001. Across samples, girls reported significantly higher rates of NSSI (8.8% reported one incident, 30.5% reported more than one incident) than did boys (7.5% reported one incident, 22.8% reported more than one incident), χ2(2, N = 1,281) = 11.76, p < .01. Chi-square analyses did not reveal developmental differences in rates of NSSI among the West Coast respondents across the 9th–12th grades. However, significant differences in NSSI rates were apparent across the ethnic groups in the West Coast sample with respect to all forms of injury, χ2(5, N = 1,026) = 15.57–51.41 (all ps < .01), except for self-biting. Students who endorsed “Black” or “Other” ethnic identities (most of whom were Native American) reported higher rates of NSSI than did White, Hispanic, Asian, and multiracial respondents.
Frequencies of Nonsuicidal Self-Injury Among Female and Male Participants in the West Coast and East Coast Samples
The means and standard deviations for perceived parental criticism, parental alienation, and delinquent behavior are presented separately by gender and sample in Table 2. A two-way multivariate analysis of variance (ANOVA; Sample × Gender) revealed significant main effects for sample source, Wilks's λ = 0.96, F(3, 1160) = 17.85, p < .001; gender, Wilks's λ = 0.96, F(3, 1160) = 17.30, p < .001; and their interaction, Wilks's λ = 0.96, F(3, 1160) = 16.36, p < .001. Follow-up univariate ANOVAs revealed that participants in the West Coast sample reported higher levels of parental criticism, F(1, 1166) = 6.71, p < .01, and of alienation, F(1, 1166) = 17.63, p < .001, than did participants in the East Coast sample. Girls endorsed higher levels of parental criticism, F(1, 1166) = 4.63, p < .05, and of alienation, F(1, 1166) = 38.41, p < .001, than did boys. One significant Sample × Gender interaction emerged, with girls in the West Coast sample reporting higher levels of parental alienation than did boys, whereas rates of alienation were lower among girls than among boys in the East Coast sample, F(1, 1166) = 24.22, p < .001.
Descriptive Data for Independent and Dependent Variables by Sample and Gender
Zero-Inflated Poisson Path Analyses
NSSI
We used procedures within the Mplus program (Version 4.1; Muthén & Muthén, 1998–2006) to determine if and how parental criticism contributed to the occurrence of NSSI (i.e., “0” representing noninjurers, “1” representing all NSSI values greater than zero) and to the frequency of NSSI once initiated (i.e., the specific value of NSSI greater than zero). The presence of a mediated pathway through parental alienation was examined in models, which showed a significant effect of criticism prior to the inclusion of the mediating pathway. The presented figures include tests of mediating paths through parental alienation.
Among girls in the West Coast sample, perceived parental criticism was associated with an increased probability of engaging in NSSI (BP(NSSI) = 0.11, SEB = 0.02, p < .05, 95% CI = 0.07, 0.16) but was not related to the frequency of NSSI once initiated (BFrequency = 0.02, SEB = 0.01, ns). When parental alienation was added to the baseline model (see Figure 1, top), the direct relation between parental criticism and the probability of NSSI was no longer significant (BP(NSSI) = 0.02, SEB = 0.03, ns, 95% CI = −0.03, 0.07). In this model, the indirect path through parental alienation (BAlienation = 0.69, SEB = 0.04, p < .001; BAlienation →P(NSSI) = 0.15, SEB = 0.02, p < .001) accounted for much of the direct relation between parental criticism and an increased probability of NSSI.
Figure 1. Zero-inflated Poisson path analysis predicting the impact of parental criticism on the probability and frequency of NSSI via alienation for female participants (n = 514; top) and for male participants (n = 465; bottom) in the West Coast sample. Coefficients reflect unstandardized point estimates, with standard errors of the estimate in parentheses. Coefficients prior to mediation are in brackets. NSSI = nonsuicidal self-injury. *p < .05. **p < .01. ***p < .001.
Among boys in the West Coast sample, perceived parental criticism was associated both with an increased probability of NSSI (BP(NSSI) = 0.08, SEB = 0.03, p < .05, 95% CI = 0.02, 0.13) and with greater repetition of NSSI once initiated (BFrequency = 0.07, SEB = 0.02, p < .01, 95% CI = 0.04, 0.11). In the mediated model (see Figure 1, bottom), neither the direct path from parental criticism to the probability of any NSSI (BP(NSSI) = 0.00, SEB = 0.03, ns, 95% CI = −0.06, 0.07) nor the direct path from parental criticism to the frequency of NSSI (BFrequency = 0.04, SEB = 0.02, ns, 95% CI = −0.01, 0.08) was significantly different from zero. As among girls, the indirect path between parental criticism and an increased probability of NSSI through parental alienation (BAlienation = 0.61, SEB = 0.05, p < .001; BAlienation →P(NSSI) = 0.12, SEB = 0.03, p < .001) accounted for much of the direct relation between parental criticism and the probability of NSSI obtained in the initial model. Similarly, the indirect path between parental alienation and the frequency of NSSI (BAlienation = 0.61, SEB = 0.05, p < .001; BAlienation →Frequency = 0.07, SEB = 0.03, p < .05) accounted for a proportion of the direct relation between parental criticism and the repetition of NSSI found in the initial model.
Similar to results for the cross-sectional models obtained in the West Coast sample, perceived parental criticism in Grades 6–8 increased the likelihood of being a self-injurer 6 years later among girls in the East Coast sample (BP(NSSI)= 0.13, SEB = 0.07, p < .05, 95% CI = 0.01, 0.26) but was not related to the frequency of girls' NSSI once initiated (BFrequency = 0.04, SEB = 0.05, ns). When parental alienation in Grades 9–11 was added to the baseline model (see Figure 2), the direct path between perceived parental criticism in middle school and the probability of NSSI in 12th grade dropped to nonsignificance (BP(NSSI) = 0.08, SEB = 0.08, 95% CI = −0.08, 0.25).
Figure 2. Zero-inflated Poisson path analysis predicting the impact of parental criticism on the probability and frequency of NSSI via alienation for female participants (n = 111) in the East Coast sample. Coefficients reflect unstandardized point estimates, with standard errors of the estimate in parentheses. Coefficients prior to mediation are in brackets. NSSI = nonsuicidal self-injury. *p < .05. ***p < .001.
Among boys, perceived parental criticism in middle school increased the likelihood of being an injurer 6 years later, though only marginally (BP(NSSI) = 0.14, SEB = 0.08, p < .10), and was not related to the frequency of boys' NSSI once initiated (BFrequency = 0.04, SEB = 0.04, ns). Because these initial effects did not reach standard levels of statistical significance, mediated models were not examined among boys in the East Coast sample.
Delinquent behavior
Among girls in the West Coast sample, the level of perceived parental criticism was significantly related to increased rule-breaking behavior (BRule = 0.29, SEB = 0.05, p < .001, 95% CI = 0.19, 0.37). When parental alienation was added to the baseline model (see Figure 3, top), the direct relation between parental criticism and rule-breaking behavior dropped to nonsignificance (BRule = 0.07, SEB = 0.06, 95% CI = −0.05, .019) as a result of the indirect path through parental alienation (BAlienation = 0.68, SEB = 0.04, p < .001; BAlienation→Rule = 0.32, SEB = 0.06, p < .001).
Figure 3. Path analysis predicting the impact of parental criticism on rule-breaking behavior via alienation for female participants (n = 514; top) and for male participants (n = 464; bottom) in the West Coast sample. Coefficients reflect unstandardized point estimates, with standard errors of the estimate in parentheses. Coefficients prior to mediation are in brackets. ***p < .001.
Among boys in the West Coast sample, the level of perceived parental criticism was significantly related to increased rule-breaking behavior (BRule = 0.24, SEB = 0.06, p < .001, 95% CI = 0.12, 0.36). When parental alienation was added to the baseline model (see Figure 3, bottom), the direct relation between parental criticism and rule-breaking behavior dropped to nonsignificance (BRule = 0.02, SEB = 0.07, 95% CI = −0.12, 0.15). The indirect path through parental alienation (BAlienation = 0.60, SEB = 0.05, p < .001; BAlienation→Rule = 0.36, SEB = 0.05, p < .001) accounted for much of the direct relation between parental criticism and increased rule-breaking behavior among boys.
As in the cross-sectional models obtained in the West Coast sample, perceived parental criticism in Grades 6–8 contributed to increased rule-breaking behavior 6 years later among girls in the East Coast sample (BRule = 0.23, SEB = 0.07, p < .001, 95% CI = 0.09, 0.38). Unlike in the West Coast sample, however, when parental alienation in Grades 9–11 was added to the baseline model (see Figure 4, top), the direct path between parental criticism in middle school and rule-breaking behavior in 12th grade remained significant (BRule = 0.23, SEB = 0.07, p < .01, 95% CI = 0.09, 0.38). The pathways making up the indirect effect through parental alienation were not significant (BAlienation = 0.07, SEB = 0.18; BAlienation →Rule = −0.03, SEB = 0.03).
Figure 4. Path analysis predicting the impact of parental criticism on rule-breaking behavior via alienation for female participants (n = 123; top) and for male participants (n = 111; bottom) in the East Coast sample. Coefficients reflect unstandardized point estimates, with standard errors of the estimate in parentheses. Coefficients prior to mediation are in brackets. *p < .05. **p < .01.
A similar pattern was found among boys in the East Coast sample, with perceived parental criticism in middle school contributing to increased rule-breaking behavior 6 years later (BRule = 0.20, SEB = 0.08, p < .01, 95% CI = 0.05, 0.35). When parental alienation in Grades 9–11 was added to the baseline model (see Figure 4, bottom), the direct path between perceived parental criticism in middle school and rule-breaking behavior in 12th grade remained significant (BRule = 0.21, SEB = 0.08, p < .01, 95% CI = 0.06, 0.36). This result follows from the pathways making up the indirect effect through parental alienation being weak or nonsignificant (BAlienation = .42, SEB = 0.18, p < .05; BAlienation→Rule = –0.02, SEB = 0.04, ns).
Discussion The Phenomenology of NSSI Among “Privileged” Youths
NSSI emerged as a prominent and recurrent phenomenon among the 1,300 children of highly educated, white-collar professionals examined here. Nearly a third of these adolescents reported engaging in NSSI during the previous year, with approximately three quarters of injurers endorsing recurrent episodes of NSSI. These rates are higher than those observed in most other school settings (Laye-Gindhu & Schonert-Reichl, 2005; Muehlenkamp & Guttierez, 2004; Ross & Heath, 2002) and may reflect one or more factors. First, heightened media attention to NSSI in recent years may have contributed to increased rates of NSSI and/or to youths' comfort with reporting it. Second, the FASM, which was used to measure NSSI in this study, captures a wider range of self-injury methods than do other measures of NSSI (e.g., body picking, skin scraping, and self-biting), which renders it highly sensitive but perhaps overly inclusive. Finally, rates of NSSI may, in fact, be elevated among upper-middle-class, suburban youths, perhaps as a function of increased pressure to contain their emotions and achieve at superior levels (Luthar & Becker, 2002).
Rates of NSSI were especially pronounced among the West Coast participants, which may qualify the generalizability of these findings. As mentioned previously, the current study was invited by school leaders in this suburban community following a series of self-destructive behaviors among local adolescents during the preceding year. It is impossible to ascertain if or how these community events may have influenced adolescents' NSSI as reported here, but they certainly warrant cautious interpretation of these high endorsement rates. Beyond community experience effects, however, much of the observed difference in NSSI rates between the West and East Coast samples may follow from the unique design features of these studies. The West Coast students were assured that their survey responses would remain anonymous, whereas the East Coast students were advised that their responses were connected with their identity and that the research team was required to report instances of significant concern for a student's safety. Thus, youths in the East Coast sample may have been more reluctant to disclose NSSI than were their West Coast counterparts. The comparable rates of delinquent behavior reported in the West and East Coast samples suggest that student reports of NSSI may be especially sensitive to data collection procedures. Despite concerns about the generalizability of these findings, the data clearly suggest that all is not well among these purportedly “privileged” and protected youths.
Beyond sample effects, gender emerged as a salient influence on rates and methods of NSSI. Although reports of NSSI were elevated among girls, the boys in these samples endorsed significant levels of NSSI. These findings replicate data from other community samples, which suggest that gender differences in rates of NSSI are more modest than previously thought (Garrison et al., 1993; Gratz et al., 2002; Tyler et al., 2003). These data point to the need for increased research and clinical attention to NSSI among boys, particularly given current evidence that gender may moderate self-injurious pathways. Similarly, there is a need for greater consideration of ethnic differentials in NSSI, given the suggestion here and elsewhere that some groups may be at disproportionately high or low risk for NSSI (Gratz, 2006; Lipschitz et al., 1999; Marshall & Yazdani, 1999; Nada-Raja et al., 2004).
Parental Criticism, Alienation, and NSSI
Beyond the descriptive level, the present findings generally support the proposed motivational pathway from parental criticism to NSSI via negative relationship representations (i.e., parental alienation). Perceived parental criticism statistically predicted NSSI in both the cross-sectional and the longitudinal samples. Moreover, adolescents' reported sense of alienation toward parents emerged as a salient process explaining these relations. In the West Coast sample, parental alienation accounted for much of the relation between perceived parental criticism and the initiation of NSSI among both girls and boys, as well as for the frequency of NSSI among boys. Longitudinal patterns in the East Coast sample provided preliminary support for the directionality of this motivational pathway. Discrepant patterns in the West and East Coast samples may reflect regional variations, distinct developmental patterns and processes, and/or unstable parameter estimates due to the small size of the East Coast sample. Although there is a need for replication studies to confirm these directional interpretations, the data support the assertion that critical parenting may contribute to negative representations of others, thereby decreasing youths' motivation to turn to others in times of duress and increasing the likelihood of NSSI as a self- and body-based coping strategy.
However, the specificity of this motivational pathway to NSSI was not supported in this study. Significant paths from perceived parental criticism to delinquent behavior via parental alienation revealed that these are important processes for understanding both self- and other-directed distress and aggression. Perceived parental criticism was related to rule-breaking behavior among girls and boys, and parental alienation played a mediating role in these relations in the West Coast sample. As with NSSI, these patterns were less consistent in the longitudinal East Coast sample, but there was preliminary support for their directionality.
Overall, the present findings are consistent with the extant literature on the role of expressed emotion, particularly parental criticism, on rates and patterns of clinical dysfunction among youths (Asarnow, Tompson, Woo, & Cantwell, 2001; McCarty et al., 2004; Wedig & Nock, 2007), as well as with recent work demonstrating the contribution of alienation to youth maladaptation (O'Donnell, Schwab-Stone, & Ruchkin, 2006; Sankey & Huon, 1999). However, this study examined a single developmental pathway, and its limited statistical power precluded the consideration of protective and/or vulnerability processes that may moderate (or mediate) these relations. For example, many of the youths who reported parental criticism in this study may have experienced overt forms of maltreatment as well. Future research must investigate other processes that influence pathways from adverse caregiving experiences to specific forms of psychopathology. Moreover, issues of specificity remain to be clarified with respect to factors that influence pathways toward different kinds of outcomes (e.g., NSSI vs. delinquency), as well as to those factors that may underlie a specific outcome in various developmental contexts (e.g., NSSI in adolescence vs. adulthood).
Strengths and Limitations
Notwithstanding the unique and complementary strengths of these cross-sectional and longitudinal, process-oriented analyses, these findings should be considered in the context of the unique features of this investigation. As noted above, this study evaluated only one developmental pathway from critical parenting to NSSI. Furthermore, although the use of youth self-reports in this study was informed by a wealth of literature pointing to the value of adolescent self-reports in studies of parent–adolescent interaction quality (De Ross, Marrinan, Schattner, & Gullone, 1999), such data have limitations, particularly when self-reports are used as the sole method of data collection (Schwartz, 1999). The monomethod, single-informant research design in this investigation may introduce concerns about shared method variance, despite the removal of shared variance across predictors in these multivariate analyses. These findings await replication in future studies using multiple methods (e.g., family observation) and informants (e.g., parents, teachers).
Our data offer a valuable view into the lives of upper-middle-class, suburban youths, but the unique features of the communities may constrain the generalizability of the present findings to other settings. For example, the measure of perceived parental criticism used here is closely connected to broader constructs related to perfectionistic tendencies and parental expectations. Thus, the present findings may reflect the undue influence of parental pressure in a context of high-achievement orientation, rather than (or in addition to) critical parenting per se. Alternatively, as with most school-based samples, these findings may be biased toward health, as more maladaptive adolescents may have refused to participate, dropped out of high school, or been enrolled in an alternative educational milieu at the time of data collection.
As discussed previously, the present findings may reflect features unique to the measure of NSSI in this study. Although the FASM has been employed in several empirical studies to date (Guertin et al., 2001; Nock & Prinstein, 2004, 2005), it is in the early stages of psychometric evaluation and validation. Moreover, this study did not include the functional portion of the FASM, which may have compromised its reliability and validity. In addition to being unable to examine functional aspects of NSSI in these samples, it is important to note, we were not able to verify that the self-injurers in this sample met the full criteria for NSSI, because we did not ask about suicidal intent.
Finally, the limited statistical power of the longitudinal analyses in this investigation constrained our ability to issue firm statements about the temporal patterning of the obtained results. Similarly, we were not able to ascertain whether patterns of NSSI differed as a function of maternal versus paternal criticism and/or of a youth's perceived alienation from mother, father, or both parents (Luthar & Latendresse, 2005). The limited size of the East Coast sample in combination with the sophistication and computational demands of the current analyses required to account for the distributional properties of the NSSI outcome may have occluded meaningful patterns in the data. Nevertheless, we believe that ZIP regression models offer an important analytic option in future studies of NSSI.
The pattern of NSSI observed here is typical of that seen in other community settings in which the preponderance of participants deny NSSI, yielding scores of zero, and a subset of respondents endorse various rates of NSSI. Researchers have long struggled to work with these kinds of nonnormal distributions; typically, they impose categorical cutoffs to dichotomize NSSI as absent or present or to trichotomize it as absent, present, or recurrent (Low et al., 2000; Whitlock et al., 2006; Yates et al., in press). However, categorical approaches may obscure meaningful distinctions in levels of NSSI, and they often rely on arbitrary frequency cutoffs. ZIP regression models offer a computationally demanding yet appropriate alternative to traditional analytic approaches. With this modeling paradigm, it is possible to hypothesize different precursors, mechanisms, and consequences regarding the initiation of NSSI versus its maintenance, escalation, or desistance over time. Thus, ZIP modeling provides a powerful tool to inform intervention efforts, as it can identify personal, social, ecological, and/or physiological forces that increase the relative resilience or fragility of individuals with regard to the initiation and/or maintenance of specific behaviors, such as a NSSI.
Clinical Implications
Clinical guidelines for practice related to NSSI have emerged over the past 5–10 years (Evans, 2000; Muehlenkamp, 2006). Building on the cognitive–behavioral work of Linehan and others (e.g., Linehan, Armstrong, Suarez, Allmon, & Heard, 1991), these approaches tend to emphasize the individual as the clinical focus. However, this investigation highlights the relevance of subtle family dynamics as salient influences on development and as promising targets for therapeutic intervention. These data suggest that incorporating the broader family system into the treatment of adolescent injurers through family therapy or concurrent parent education may provide incremental utility to more traditional treatments.
Beyond attending to the parent–adolescent relationship, the present findings suggest that treatments that adopt a critical- or shame-based approach to practice may inadvertently reinforce a heretofore unrecognized force underlying NSSI. Parents, teachers, and clinicians often localize NSSI within the adolescent, as they fail to recognize that NSSI follows from multivariate transactions between the adolescent and her or his environments. Thus, applied work with self-injuring youths must incorporate psychoeducation to help parents and other stakeholders recognize the multifaceted psychosocial systems, including NSSI, that influence adolescent behavior. Moreover, strength-based approaches to treatment will empower caregivers to effect positive changes in their families and communities to support youths. Just as the family or community environment may instantiate vulnerabilities to NSSI, so too might these systems buffer or prevent such pathways. Research that clarifies processes that promote resistance to, or desistance from, pathological pathways toward NSSI will inform efforts to develop strength- and competence-based approaches to practice (Yates & Masten, 2004).
Still, even the best services will do little to help self-injurers if they are not utilized. It is rare for those who self-injure to seek psychological services (Whitlock et al., 2006), and this is likely to be especially true in adolescence, when youths have few resources to seek services independently. This reticence to seek services, coupled with the pernicious and pervasive tendency for clinicians, school administrators, policymakers, and parents to overlook signs of distress among high-achieving, high-income youths, is a recipe for disaster (Luthar, 2003). The present findings join a broader cadre of evidence that distress and pathology are thriving within seemingly pristine and protected communities. Moreover, the driving forces underlying adolescent NSSI among upper-middle-class, suburban youths (and likely other youths) extend beyond the individual to include the family system and, perhaps, broader systems of influence (e.g., peers, media). In closing, we echo prior calls to offer multifaceted services targeting these “privileged” yet pained youths, their families, and their communities (Luthar, 2003).
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Submitted: February 9, 2007 Revised: August 10, 2007 Accepted: August 20, 2007
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Source: Journal of Consulting and Clinical Psychology. Vol. 76. (1), Feb, 2008 pp. 52-62)
Accession Number: 2008-00950-008
Digital Object Identifier: 10.1037/0022-006X.76.1.52
Record: 34- Title:
- Nonsuicidal self-injury as a time-invariant predictor of adolescent suicide ideation and attempts in a diverse community sample.
- Authors:
- Guan, Karen. University of North Carolina at Chapel Hill, Department of Psychology, Chapel Hill, NC, US
Fox, Kathryn R.. University of North Carolina at Chapel Hill, Department of Psychology, Chapel Hill, NC, US
Prinstein, Mitchell J.. University of North Carolina at Chapel Hill, Department of Psychology, Chapel Hill, NC, US, mitch.prinstein@unc.edu - Address:
- Prinstein, Mitchell J., University of North Carolina at Chapel Hill, Department of Psychology, Davie Hall, Campus Box 3270, Chapel Hill, NC, US, 27599-3270, mitch.prinstein@unc.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 80(5), Oct, 2012. pp. 842-849.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 8
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- adolescents, nonsuicidal self-injury, suicide attempts, suicide ideation, depression
- Abstract:
- Objective: Longitudinal data on adolescent self-injury are rare. Little is known regarding the associations between various forms of self-injurious thoughts and behaviors over time, particularly within community samples that are most relevant for prevention efforts. This study examined nonsuicidal self-injury (NSSI) as a time-invariant, prospective predictor of adolescent suicide ideation, threats or gestures, and attempts over a 2.5-year interval. Method: A diverse (55% female; 51% non-White) adolescent community sample (n = 399) reported depressive symptoms, frequency of NSSI, suicide ideation, threats or gestures, and attempts in 9th grade (i.e., baseline) and at 4 subsequent time points. Generalized estimating equations and logistic regressions were conducted to reveal the associations between baseline NSSI and the likelihood of each suicidal self-injury outcome postbaseline while controlling for depressive symptoms and related indices of suicidal self-injury as competing predictors. Results: Baseline NSSI was significantly, prospectively associated with elevated levels of suicide ideation and suicide attempts, but not threats or gestures. Neither gender nor ethnicity moderated results. Conclusions: Above and beyond established risk factors such as depressive symptoms and previous suicidality, adolescent NSSI may be an especially important factor to assess when determining risk for later suicidality. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Adolescent Psychology; *Attempted Suicide; *Self-Injurious Behavior; *Suicidal Ideation; *Suicide; Major Depression; Risk Factors
- Medical Subject Headings (MeSH):
- Adolescent; Depression; Female; Humans; Male; Predictive Value of Tests; Prospective Studies; Risk Factors; Self-Injurious Behavior; Suicidal Ideation; Suicide, Attempted
- PsycINFO Classification:
- Behavior Disorders & Antisocial Behavior (3230)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adolescence (13-17 yrs)
- Tests & Measures:
- Mood and Feelings Questionnaire
- Grant Sponsorship:
- Sponsor: National Institutes of Health
Grant Number: R01-MH85505; R01-HD055342
Recipients: Prinstein, Mitchell J. - Methodology:
- Empirical Study; Longitudinal Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Jul 30, 2012; Accepted: Jun 18, 2012; Revised: Jun 11, 2012; First Submitted: Nov 1, 2011
- Release Date:
- 20120730
- Correction Date:
- 20120924
- Copyright:
- American Psychological Association. 2012
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0029429
- PMID:
- 22845782
- Accession Number:
- 2012-20362-001
- Number of Citations in Source:
- 40
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-20362-001&site=ehost-live">Nonsuicidal self-injury as a time-invariant predictor of adolescent suicide ideation and attempts in a diverse community sample.</A>
- Database:
- PsycINFO
Nonsuicidal Self-Injury as a Time-Invariant Predictor of Adolescent Suicide Ideation and Attempts in a Diverse Community Sample
By: Karen Guan
Department of Psychology, University of North Carolina at Chapel Hill
Kathryn R. Fox
Department of Psychology, University of North Carolina at Chapel Hill
Mitchell J. Prinstein
Department of Psychology, University of North Carolina at Chapel Hill;
Acknowledgement: This work was supported in part by National Institutes of Health Grants R01-MH85505 and R01-HD055342 awarded to Mitchell J. Prinstein.
Self-injurious thoughts and behaviors are remarkably dangerous yet relatively understudied phenomena (Prinstein, 2008). In particular, relatively few longitudinal data are available to understand prospective predictors of self-injury. Moreover, research studies rarely discretely identify and examine associations among the multiple types of self-injurious thoughts and behaviors that have been identified in the literature (e.g., nonsuicidal self-injury [NSSI], suicide ideation, threats, gestures, attempts, etc.; Nock & Kessler, 2006; Silverman, Berman, Sanddal, O'Carroll, & Joiner, 2007). Consequently, relatively little is known regarding the predictors of self-injury beyond broad, distal factors (e.g., depressive symptoms, prior self-injury, substance use), particularly in adolescence (Nock, 2009; Prinstein, 2008).
However, a substantial body of research recently has emerged on at least one form of self-injury: NSSI. Defined as behavior that is direct, deliberate, and not socially sanctioned, NSSI causes damage to one's body tissue and is enacted without the intent to die (Nock, 2010). NSSI is remarkably prevalent, especially among adolescents. Lifetime prevalence rates in community samples range from 15.9% to 21.2% (e.g., Muehlenkamp & Gutierrez, 2004; Ross & Heath, 2002). Studies of NSSI have proliferated and gained added clinical importance for at least three reasons. First, NSSI is a proposed diagnostic category in the draft DSM-V (Selby, Bender, Gordon, Nock, & Joiner, 2012). Second, NSSI is associated concurrently with suicidal thoughts and behaviors (Andover & Gibb, 2010; Klonsky & Olino, 2008; Lloyd-Richardson, Perrine, Dierker, & Kelley, 2007; Nock, Joiner, Gordon, Lloyd-Richardson, & Prinstein, 2006). Third, recent theories suggest that NSSI may be a risk factor for later suicidal thoughts and behaviors (Brent, 2011; Joiner, 2005). The latter hypothesis has been examined infrequently, however.
The present study examines NSSI as a long-term, time-invariant longitudinal risk factor for suicide ideation, threats or gestures, and attempts during an adolescent transition period associated with increased risk for suicidality. Epidemiological data suggest that suicide ideation is remarkably prevalent during the high school aged adolescent period (13.8%; Centers for Disease Control and Prevention [CDC], 2010). The prevalence of suicide attempts is also quite high (6.3%; CDC, 2010). Between the age groups of 10–14 to 15–19 years, the rate of completed suicide increases almost sevenfold (from 1.1 to 7.4 per 100,000; CDC, 2008). In addition, suicide is the third leading cause of death for children and adolescents aged 10–19 years (CDC, 2008). Less is known about the prevalence of suicide threats or gestures, sometimes defined as “self-injury in which there is no intent to die, but instead an intent to give the appearance of a suicide attempt in order to communicate with others” (Nock & Kessler, 2006, p. 616), or as “any interpersonal action, verbal or nonverbal, without a direct self-injurious component, that a reasonable person would interpret as communicating or suggesting that suicidal behavior might occur in the near future” (Silverman et al., 2007, p. 268). Self-injurious behaviors such as threats or gestures and uncompleted suicide attempts are crucial outcomes for investigation, as they place an enormous burden on emergency health care systems, cause significant distress among family and friends, and are the strongest predictors of eventual suicide (Cvinar, 2005; Joiner et al., 2005; Olfson, Gameroff, Marcus, Greenberg, & Shaffer, 2005).
Recent theoretical and empirical work suggests that NSSI may be associated with increased suicidal capability (Franklin, Hessel, & Prinstein, 2011; Joiner, 2005). Joiner (2005) suggested that repeated episodes of painful and provocative experiences, such as cutting or burning, may habituate those who engage in NSSI to higher levels of pain. This habituation, or acquired capability for suicide, may act as a vulnerability that, when combined with desire for suicide, has been found to significantly predict suicidal behaviors, including attempts and completed suicides (Anestis & Joiner, 2011; Joiner et al., 2009; Nademin et al., 2008; Van Orden, Witte, Gordon, Bender, & Joiner, 2008).
Preliminary empirical findings offer some initial support for NSSI as a concurrent correlate of suicidal thoughts and behavior. Higher frequencies of NSSI concurrently are associated with higher levels of suicide ideation and a history of suicide attempts (Andover & Gibb, 2010; Klonsky & Olino, 2008; Lloyd-Richardson et al., 2007; Nock et al., 2006).
Few studies have examined the longitudinal association among various forms of self-injury (e.g., Asarnow et al., 2011; Cooper et al., 2005; Owens, Horrocks, & House, 2002; Wilkinson, Kelvin, Roberts, Dubicka, & Goodyer, 2011). In some cases, different forms of self-injury (i.e., suicidal vs. nonsuicidal) have not been explicitly differentiated. For example, Cooper and colleagues (2005) revealed a prospective relationship between any self-harm episode resulting in a hospitalization (including NSSI or suicidal self-injury) and later completed suicide. Similarly, Owens and colleagues (2002) reviewed multiple studies examining the relationship between various forms of self-harm and later nonfatal or fatal self-harm; findings suggested elevated risk for suicide among self-harm patients as compared to the general population.
Some short-term longitudinal data examining associations specifically between NSSI and suicidal thoughts or behaviors in clinically referred populations also have been reported recently. For example, NSSI has been associated with slower decreases in suicide ideation in the 9 months following hospital discharge (Prinstein et al., 2008). Asarnow and colleagues (2011) and Wilkinson and colleagues (2011) revealed NSSI frequencies to be a stronger predictor of suicide attempts than were previous suicide attempts over a 24- and 28-week period, respectively.
This study aims to offer at least five unique contributions to the literature examining putative risk associated with NSSI. First, this study uses a definition of NSSI consistent with contemporary theory; thus, it has been possible to examine NSSI (specifically without suicidal intent) as a predictor of suicidal thoughts and behaviors. Second, this study focuses on a diverse community sample, offering findings that are most relevant for prevention efforts. Third, this study has been designed to examine the long-term longitudinal prediction of suicidal thoughts and behaviors from NSSI. A 2.5-year longitudinal interval has been used to examine hypotheses. Fourth, multiple outcomes reflecting discrete suicidal thoughts and behaviors (i.e., suicide ideation, threats or gestures, and attempts) are examined. Last, and perhaps most importantly, this study examines NSSI as a predictor of suicidal thoughts and behaviors while controlling for depressive symptoms and related suicidality as competing predictors. This stringent examination offers a robust test of NSSI as a predictor of later suicidal thoughts and behaviors.
It was hypothesized that NSSI would be associated with a higher likelihood of suicidal thoughts over time. Although prior longitudinal research on the developmental course of suicidal ideation is rare, empirical data suggest that ideation may occur episodically, perhaps in conjunction with major depressive episodes (Prinstein et al., 2008; Williams, Crane, Barnhofer, Van der Does, & Segal, 2006). A primary aim of this study was to examine NSSI as a longitudinal predictor of clinically significant levels of suicide ideation. Thus, data were coded to reflect severe levels of suicide ideation, and an analytic approach allowing for the examination of repeated occurrences nested within individuals was employed. It also was hypothesized that NSSI would be associated with higher occurrences of suicide threats or gestures and suicide attempts over time.
Past research has suggested adolescent girls and Latino Americans are more likely than adolescent boys and non-Latino Americans to report suicide ideation, attempts, and depressive symptoms (CDC, 2010). However, there are few extant theories suggesting that the magnitude of the association between NSSI and suicidal thoughts and behaviors may vary with respect to gender or ethnicity. Therefore, each of these demographic variables was explored as a moderator of hypothesized associations for descriptive purposes.
Method Participants
A total of 399 ninth-grade adolescents (54.8% girls) participated in the study. The ethnic distribution of the sample was 49.2% Caucasian, 22.7% African American, 19.3% Latino American (of which 64% were from Mexico, and 8% each were from Puerto Rico, Honduras, and El Salvador), and 8.8% other/mixed ethnicity within a city of lower-class socioeconomic status. According to school records, approximately 67% of students in this district were eligible for free or reduced-price lunch. Approximately 19% of adolescents reported that their parents were never married; 32% reported that their parents had separated or divorced. The majority of adolescents reported that they lived in a household with two adults (47% with two biological parents, 30% with a parent and a stepparent, grandparent, or other relative); 23% reported living in a single-parent household.
Procedure
All students in ninth grade from three rural high schools in a single county were recruited for participation (N = 712), with the exception of students in self-contained special education classes. A letter of consent initially was distributed to each adolescent's family followed by a series of reminders and additional letters distributed by school and research personnel. Response forms included an option for parents to grant or deny consent; adolescents were asked to return their signed response forms regardless of their parents' decision. Numerous adolescent-, teacher-, and school-based incentives were used to ensure the return of these consent forms (i.e., candy for each returned consent form, $30 gift card raffles during each week of recruitment, one $300 gift card grand prize raffle). Consent forms were returned by 75% of families (n = 533); of these, 80% of parents gave consent for their child's participation (n = 423). Data were unavailable for 24 participants due to changing schools (n = 18), student absenteeism on the days of testing (n = 2), or declining to participate (n = 4), yielding a Time 1 sample of 399 (56% of total population). Adolescent assent was requested at the start of data collection, following written and verbal descriptions of the study procedures. All procedures were approved by the university human subjects committee.
Measures were administered in the spring of ninth grade and then every 6 months for a total of five time points (until the spring of 11th grade). Retention varied between 90% and 99% between adjacent time points. Retention between Times 1 and 5 was 77%; 67% of attrition was due to students withdrawing from school. Attrition analyses revealed no significant differences on any study variable between adolescents who participated at one versus all time points, with one exception: Latino American adolescents were less likely to have complete data than non-Latino American adolescents, χ2(1) = 6.06, p = .01. All 399 cases were used in analyses; maximum-likelihood methods were used to account for missing data. Generalized estimating equations (GEEs) used all available data from the full sample (n = 399). Analyses conducted with only available data revealed an identical pattern of findings.
Measures
All measures were administered in adolescents' classrooms or school auditoriums. Measures were completed in a group setting, and researchers ensured that participants had sufficient privacy to complete questionnaires confidentially (e.g., participants were placed several seats and rows apart). Measures of suicide ideation, threats or gestures, and attempts were administered at all five time points. Measures of depressive symptoms and NSSI were administered at Time 1.
Nonsuicidal self-injury (NSSI)
Adolescents reported the frequency that they engaged in six types of nonsuicidal self-injurious behaviors (i.e., cut/carved skin, hit self, burned skin, inserted objects under skin, scraped/picked skin, bit self) without intending to die in the past year (Prinstein et al., 2008). The anchors for this scale were adapted from the aforementioned study to allow more accurate reporting (1 = Never, 2 = 1–2 times, 3 = 3–5 times, 4 = 6–9 times, 5 = 10 or more times). Prior research supports the concurrent validity of this assessment through significant associations with other measures of NSSI (Prinstein et al., 2008).
Suicide ideation, threats or gestures, and attempts
Suicide ideation was assessed using an adaptation of a 15-item measure (Heilbron & Prinstein, 2010). The version of the measure employed in the present study included the same eight items assessing thoughts about suicide (e.g., “I thought about death,” “I thought about how I would kill myself,” “I thought that killing myself would solve my problems”) interspersed with seven filler items from the Reasons for Living scale (Linehan, Goodstein, Nielsen, & Chiles, 1983). Suicide ideation within the past year was assessed at baseline, and ideation within the past 6 months was assessed at each follow-up time point. This composite measure included suicide ideation items drawn from the Suicidal Ideation Questionnaire (Reynolds, 1988), and the National Institute of Mental Health Diagnostic Interview Schedule for Children Version IV (Shaffer, Fisher, Lucas, Dulcan, & Schwab-Stone, 2000). Each item is scored on a 5-point scale ranging from 1 (Never) to 5 (Almost every day); higher scores are indicative of higher frequencies of suicide ideation. Internal consistency (α) across all time points ranged between .83 and .94.
Suicide threats or gestures were measured with a single item added to the above instrument (“I tried to make someone believe that I might end my life, but I didn't do it”). Adolescents responded to this item using the same 5-point scale.
Suicide attempts also were assessed with a binary item asking whether adolescents “have tried to kill themselves” in the past year (at baseline) and the past 6 months (at each follow-up time point). Two indices were computed, representing the presence of a recent suicide attempt at baseline and the presence of at least one suicide attempt between Times 2 through 5.
Depressive symptoms
Depressive symptoms were assessed using the Mood and Feelings Questionnaire (MFQ), a 33-item self-report measure designed to assess criteria for depression in children and adolescents ages 8–18 (Costello & Angold, 1988). MFQ items include statements such as “I felt miserable or unhappy,” “I cried a lot,” and “I thought bad things would happen to me.” Each item is scored on a 3-point scale: mostly true, sometimes true, or not true for the individual over the past 2 weeks. Higher scores are indicative of higher levels of depressive symptoms. In the present study, internal consistency was excellent (α =. 93).
Data Analysis
Three sets of analyses were conducted to examine study hypotheses. Descriptive statistics first were conducted to examine frequencies, gender and ethnic differences, and correlations among continuous primary variables across all five time points.
Second, GEEs were used to account for clustered within-person observations in analyses predicting the occurrence of suicide ideation and threats or gestures. As may be expected based on previous research, inspection of data for suicide ideation and threats or gestures revealed that neither construct was best characterized by linear growth over time (Prinstein et al., 2008; Williams et al., 2006). In other words, it was rare for individuals to report gradual, linear increases (or decreases) in the frequency of suicide ideation or threats or gestures across the five time points of the study. Rather, suicide ideation and threats or gestures each occurred in a sporadic manner, with intermittent peaks usually surrounded by periods of low or absent suicidality. Thus, a binary logistic outcome was modeled with an autoregressive correlation matrix structure. This procedure allowed for multiple occurrences (i.e., elevated ideation, threats/gestures) within individuals. Analyses revealed no association between time and elevated levels of ideation or threats/gestures, suggesting no change in the hazard as a function of elapsed time.
Because the literature currently offers no consistent data to suggest a meaningful cutoff score indicating elevated suicide ideation, occurrences of elevated ideation were computed in two ways. First, based on clinical judgment, it was determined that any adolescent reporting a frequency of suicide ideation “at least once per week” or “almost every day” would be of strong clinical concern. Thus, within each time point, adolescents who reported a score of 4 or 5 on any suicide ideation item were defined as having elevated suicide ideation. This yielded a total of approximately 8% of the sample with elevated ideation at each time point. Notably, recent data suggest that 13.8% of high school aged adolescents report “seriously considered attempting suicide” within a 1-year interval (CDC, 2010). Thus, our estimate was conservative. A second computation for determining elevated suicide ideation occurrences was statistically based. A single grand mean and standard deviation of suicide ideation scores across person and time were computed. Scores one standard deviation above this grand mean were coded to reflect elevated suicide ideation. Between 5% and 9% at each time point exceeded this cutoff score. All analyses below were conducted twice, using each cutoff score, respectively. The pattern of findings was identical. To offer more utility for clinical purposes, analyses using the former approach for establishing elevated suicide ideation scores are presented below.
A similar procedure was used to dichotomize suicide threat or gesture scores. Any suicide threat or gesture is of clinical concern. Thus, any response indicating that adolescents engaged in a suicide threat or gesture more often than “never” was included as an occurrence. A range of 2% to 7% of adolescents reported an occurrence of suicide threat or gesture at each time point.
In both GEE models prospectively predicting ideation and threats or gestures, respectively, analyses included occurrences postbaseline (i.e., at Time Points 2, 3, 4, and 5) as a dependent measure. All measures of related baseline suicidality (i.e., ideation, threats or gestures, and suicide attempts) were included as independent variables, as were dummy codes for gender (female), African American, and Latino American adolescents. In addition, main effects of baseline depressive symptoms were entered, followed by a test of baseline NSSI as an independent variable. Interactions examining gender and ethnicity as moderators initially were examined (i.e., Gender × NSSI, African American [dummy coded] × NSSI, Latino American [dummy coded] × NSSI [dummy coded]); however, none reached significance, and thus, they are not reported below. The final model for suicidal ideation is shown below; the model for threats or gestures was identical in structure.
Variables with the prefix BL represent baseline measures, i indicates the individual, and t indicates the time point (e.g., Postbaseline Assessments 2, 3, 4, or 5). For each outcome variable, suicidal ideation and threats or gestures, an autoregressive working correlation structure was specified for the residuals to account for the dependence in the repeated measures.
Third, a logistic regression analysis was conducted to examine the prediction of suicide attempts. Because suicide attempts were low in frequency (i.e., between one and nine at each time point), a single outcome variable was computed identifying adolescents who did or did not report a suicide attempt at any point between Times 2 and 5. A logistic regression analysis, using all of the same predictors described above, was conducted to examine prospective prediction of suicide attempts. Because a single measure of attempts was taken for each individual, this analysis did not require the use of GEE (or a working correlation structure) and was fitted to the data using the typical maximum-likelihood estimator under the assumption of independent observations.
Results Preliminary Analyses
Table 1 presents descriptive statistics for all study variables, as well as the results of t tests and chi-square tests examining gender differences. Results indicated that a range of 7.1% to 8.9% of adolescents reported elevated levels of suicide ideation and 1.5% to 6.9% reported engaging in suicide threats or gestures at each time point. At baseline, 29.5% reported that they had engaged in NSSI; 3.3% of adolescents reported that they had attempted suicide in the past year, and 5.2% of adolescents reported having attempted suicide across the follow-up period. Females reported significantly higher levels of baseline depressive symptoms than males as well as higher levels of baseline NSSI. No significant gender differences were found for suicide ideation, threats or gestures, or attempts at any time point. Tests of ethnic differences revealed that African Americans and Latino Americans reported significantly higher levels of baseline depressive symptoms than Whites/Caucasians, t(398) = 2.51, p = .01, and t(398) = 2.71, p < .01, respectively. In addition, at Time 2, significantly more Latino Americans reported engaging in a suicide threat or gesture as compared to other ethnic groups, χ2(1) = 5.69, p < .05; at Time 5, African Americans reported significantly higher levels of suicide ideation as compared to other ethnic groups, χ2(1) = 8.74, p < .01. No significant ethnic group differences were found for NSSI, suicide ideation, threats or gestures, or attempts at all other time points.
Descriptive Statistics and Tests of Gender Differences for All Primary Variables
Table 2 presents correlations among continuous primary variables of depressive symptoms, NSSI, and suicide ideation. As expected, all variables were significantly positively associated across time.
Bivariate Associations Among Continuous Primary Variables
Longitudinal Prediction of Suicide Ideation
Table 3 displays parameter estimates from two GEEs predicting suicide ideation and threats or gestures, respectively. Results for suicide ideation revealed that after controlling for baseline self-injurious thoughts and behaviors and depressive symptoms, African American adolescents were about a third as likely to report an occurrence of elevated suicide ideation over the follow-up period. Consistent with hypotheses, results suggested that after accounting for these other effects, each additional point in reported NSSI at baseline was associated with a more than fivefold increase in the odds of a future occurrence of elevated suicide ideation. Interestingly, with this association between NSSI and later suicide ideation included, there was no significant association between baseline ideation and ideation occurrences at follow-up. In addition, no significant effects were revealed for other demographic predictors (i.e., gender, Latino American ethnicity), other measures of self-injury (i.e., baseline suicide threats or gestures, baseline suicide attempts), or baseline depressive symptoms.
Generalized Estimating Equation Results Predicting Suicide Ideation and Threats or Gestures During Follow-Up From Demographic Variables, Baseline Suicidal Thoughts and Behaviors, Depressive Symptoms, and Nonsuicidal Self-Injury
Longitudinal Prediction of Suicide Threats or Gestures
Results for the prediction of suicide threats or gestures revealed several main effects. Females were about half as likely as male adolescents to report the occurrence of a suicide threat or gesture during the follow-up interval. Additionally, African American adolescents were approximately a third as likely to report a suicide threat or gesture during follow-up. No other significant effects were revealed.
Longitudinal Prediction of Suicide Attempts
Consistent with past research, analyses revealed that prior suicide attempts were associated with future suicide attempts. Results suggested that a suicide attempt at baseline was associated with a nearly ninefold increase in the likelihood of a suicide attempt over the follow-up period. After controlling for this effect, and also consistent with prior work, results suggested that being female was associated with a nearly twofold increase in the likelihood of later suicide attempts, and each 1- point increase in depressive symptoms was associated with a fourfold increase in the likelihood of future suicide attempts. Interestingly, results suggested that after accounting for each of these effects, each additional-unit increase in reported NSSI was associated with a sevenfold increase in the likelihood of future suicide attempts. No other significant effects were revealed (see Table 4). Importantly, suicide attempts were reported at each separate time point. The vast majority occurred in the first year of follow-up (of 19 attempts during the follow-up period, eight were at Time 2, nine at Time 3, one at Time 4, and one at Time 5). Thus, it should be noted that each of these predictors was mostly associated with the likelihood of attempts occurring within 1 year of the baseline assessment.
Logistic Regression Results Predicting Suicide Attempts During Follow-Up From Demographic Variables, Baseline Attempts, Depressive Symptoms, and Nonsuicidal Self-Injury
DiscussionNSSI is an important behavior to understand and prevent in adolescence in its own right. Researchers have suggested that NSSI also may be an important predictor of later suicidal behavior; however, this hypothesis has not received substantial empirical attention. The current study offers compelling evidence from a diverse community-based sample suggesting that higher frequencies of NSSI are indeed associated with significantly increased risks of suicide ideation and attempts, but not threats or gestures. These results are particularly notable given the strong theoretical overlap and moderate correlations among NSSI, depressive symptoms, and suicide thoughts and behaviors. Results offer a useful evidence-based tool for clinicians attempting to assess the risk of suicidal behavior among adolescent clients; specifically, a past history of NSSI offers an important contribution to risk assessment above and beyond the role of prior suicidality and depressive symptoms as risk factors.
Results may be interpreted in light of several theoretical perspectives. First, as hypothesized by Joiner (2005), NSSI may be an experience that promotes adolescents' acquired capability for suicide. In other words, adolescents who engage in NSSI may develop an increased tolerance for pain and a decreased fear of death. NSSI also may promote a habituation to self-injurious behaviors, the development of more positive attributions regarding self-injury, or behavioral reinforcement through perceived social or internal rewards for self-injury. Any of these factors may mediate the association between NSSI and later suicidality (e.g., Franklin et al., 2011; Hooley, Ho, Slater, & Lockshin, 2010). Theories regarding acquired capability for suicidality do not specify the precise mechanism that is responsible for the link between early painful/provocative experiences and later suicidality, nor was this a focus of the present study. However, this would be an important area for further exploration.
An alternate explanation for observed results pertains to possible third-variable factors that may be responsible for both NSSI and suicidal self-injurious thoughts/behaviors. For instance, more frequent stressful experiences or a deterioration of adaptive coping skills may be responsible for both the occurrence of NSSI and later suicidal behavior. Neither of these factors may be fully accounted for by the presence of depressive symptoms and prior suicidality in our models.
Interestingly, NSSI was not associated prospectively with the occurrence of suicide threats or gestures. Prior research has suggested that suicide ideation, threats or gestures, and attempts are discrete constructs with unique correlates (e.g., Nock & Kazdin, 2002; Nock & Kessler, 2006). The results from this study partially support this idea, perhaps suggesting that suicide threats or gestures are motivated by different processes than are suicide ideation and attempts. Among adolescents, suicide ideation and attempts do not always reflect a true desire to die (Silverman et al., 2007). However, suicide threats or gestures, by their definition, may be even less motivated by suicidal intent. As they have been defined in the literature, suicide threats or gestures seem to be interpersonally directed, and may be more closely associated with NSSI serving social functions rather than NSSI primarily addressing automatic, internal functions (Nock & Prinstein, 2004). As the present study measured this construct with only a single item, it also is possible that the lack of significant findings is simply due to poor validity in our measurement of threats or gestures. More comprehensive assessments of self-injurious thoughts and behaviors are needed in future work.
Results regarding gender and ethnicity as main effects or moderators of findings have been presented. As demonstrated in prior research, being female was related to an increased risk of future suicide attempts (CDC, 2010). Also consistent with prior research, our results suggest that African American adolescents were at less risk of future suicide ideation and threats or gestures (e.g., Joe, Baser, Breeden, Neighbors, & Jackson, 2006). No findings suggest that demographic factors moderated the association between NSSI and future self-injurious thoughts and behavior. Combined with emerging evidence suggesting consistency in the frequency of NSSI across gender, ethnicity, and multiple cultures (Giletta, Scholte, Engles, Ciairano, & Prinstein, 2012), results suggest that NSSI is a phenomenon that may present similar risks across multiple populations of youth. However, as these analyses were exploratory, replication is needed to better explain the role of gender and ethnicity as moderators of suicidal behaviors over time.
In our analyses, baseline ideation was not a significant predictor of later ideation in the presence of all other predictors (e.g., NSSI, depressive symptoms, gender). One interpretation of this counterintuitive result is that suicidal thoughts may be episodic in nature (Prinstein et al., 2008; Williams et al., 2006); thus, stability in these constructs may not be expected across time across all possible time points. This finding should be replicated before drawing concrete conclusions.
Overall, results offer empirical evidence for the importance of NSSI as a construct that has predictive value in assessing risk for adolescent suicide ideation and attempts. Future research on this topic would benefit from addressing some of the limitations in this study. First, the generality of results may have been compromised by the relatively low participation rate recruited and retained in this sample. Although the sample compares quite favorably to other low-income, ethnically diverse longitudinal samples, the overall rate of participation nevertheless limits confidence in applying these results to all populations. Second, we assessed all self-injurious thoughts and behaviors using adolescent self-report, and two constructs were measured using single-item indices. While this method allowed us to assess a large number of adolescents, future studies also could include parent reports of adolescents' self-injury, as well as more thorough instruments examining multiple self-injury constructs. Too often in the literature, self-injury is assessed in a brief, cursory manner that does not allow for a careful delineation of the different forms of self-injury that have been identified as discrete constructs in past research (e.g., Nock & Kazdin, 2002; Nock & Kessler, 2006). Third, results regarding significant associations over time do not imply causal links between NSSI and these other self-injurious outcomes. This is a common limitation for studies of this type.
In a relatively brief period of time, NSSI has become a widely prevalent behavior, particularly among adolescents (Nock, 2010). Accordingly, the study of NSSI has become a burgeoning research area. Results from this study suggest that NSSI may be a notable risk factor for future suicide ideation and attempts during a developmental period known to be associated with heightened risk for self-injury. Understanding why some adolescents who engage in NSSI are at risk for suicidal self-injury, while others are not, will be an important direction for research and clinical efforts.
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Submitted: November 1, 2011 Revised: June 11, 2012 Accepted: June 18, 2012
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Source: Journal of Consulting and Clinical Psychology. Vol. 80. (5), Oct, 2012 pp. 842-849)
Accession Number: 2012-20362-001
Digital Object Identifier: 10.1037/a0029429
Record: 35- Title:
- Older maternal age is associated with depression, anxiety, and stress symptoms in young adult female offspring.
- Authors:
- Tearne, Jessica E.. Telethon Kids Institute, The University of Western Australia, Crawley, WAU, Australia, jessica.tearne@research.uwa.edu.au
Robinson, Monique. Telethon Kids Institute, The University of Western Australia, Crawley, WAU, Australia
Jacoby, Peter. Telethon Kids Institute, The University of Western Australia, Crawley, WAU, Australia
Allen, Karina L.. School of Psychology, The University of Western Australia, Crawley, WAU, Australia
Cunningham, Nadia K.. School of Psychology, The University of Western Australia, Crawley, WAU, Australia
Li, Jianghong. WZB Berlin Social Research Center, Germany
McLean, Neil J.. School of Psychology, The University of Western Australia, Crawley, WAU, Australia - Address:
- Tearne, Jessica E., School of Psychology, The University of Western Australia, M304, 35 Stirling Highway, Crawley, WAU, Australia, 6009, jessica.tearne@research.uwa.edu.au
- Source:
- Journal of Abnormal Psychology, Vol 125(1), Jan, 2016. pp. 1-10.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 10
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- DASS, Raine Study, cohort study, maternal age, paternal age
- Abstract (English):
- The evidence regarding older parental age and incidence of mood disorder symptoms in offspring is limited, and that which exists is mixed. We sought to clarify these relationships by using data from the Western Australian Pregnancy Cohort (Raine) Study. The Raine Study provided comprehensive data from 2,900 pregnancies, resulting in 2,868 live born children. A total of 1,220 participants completed the short form of the Depression Anxiety Stress Scale (DASS-21) at the 20-year cohort follow-up. We used negative binomial regression analyses with log link and with adjustment for known perinatal risk factors to examine the extent to which maternal and paternal age at childbirth predicted continuous DASS-21 index scores. In the final multivariate models, a maternal age of 30–34 years was associated with significant increases in stress DASS-21 scores in female offspring relative to female offspring of 25- to 29-year-old mothers. A maternal age of 35 years and over was associated with increased scores on all DASS-21 scales in female offspring. Our results indicate that older maternal age is associated with depression, anxiety, and stress symptoms in young adult females. Further research into the mechanisms underpinning this relationship is needed. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Impact Statement:
- This study suggests that older maternal age is associated with adverse symptoms of depression, anxiety, and distress in young adult females. Paternal age was not found to be associated with mental health outcomes for either males or females in this sample. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Age Differences; *Fathers; *Major Depression; *Mothers; *Stress; Adult Offspring; Anxiety; Risk Factors; Symptoms
- PsycINFO Classification:
- Affective Disorders (3211)
- Population:
- Human
Male
Female - Location:
- Australia
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs) - Tests & Measures:
- Depression Anxiety Stress Scales
Beck Anxiety Inventory DOI: 10.1037/t02025-000
Beck Depression Inventory DOI: 10.1037/t00741-000 - Grant Sponsorship:
- Sponsor: University of Western Australia, Australia
Other Details: Completion Scholarship
Recipients: Tearne, Jessica E.
Sponsor: National Health and Medical Research Council, Australia
Other Details: Early Career Fellowship
Recipients: Robinson, Monique
Sponsor: Raine Medical Research Foundation
Recipients: No recipient indicated
Sponsor: The University of Western Australia, Australia
Recipients: No recipient indicated
Sponsor: Telethon Kids Institute, Australia
Recipients: No recipient indicated
Sponsor: University of Western Australia, Faculty of Medicine, Dentistry and Health Sciences, Australia
Recipients: No recipient indicated
Sponsor: Women and Infants Research Foundation
Recipients: No recipient indicated
Sponsor: Curtin University of Technology, Australia
Recipients: No recipient indicated - Methodology:
- Empirical Study; Followup Study; Quantitative Study
- Supplemental Data:
- Tables and Figures Internet
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
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Older Maternal Age Is Associated With Depression, Anxiety, and Stress Symptoms in Young Adult Female Offspring
By: Jessica E. Tearne
Telethon Kids Institute and School of Psychology, The University of Western Australia;
Monique Robinson
Telethon Kids Institute, The University of Western Australia
Peter Jacoby
Telethon Kids Institute, The University of Western Australia
Karina L. Allen
School of Psychology, The University of Western Australia
Nadia K. Cunningham
School of Psychology, The University of Western Australia
Jianghong Li
WZB Berlin Social Research Center, Germany, and Centre for Population Health Research, Faculty of Health Sciences, Curtin University
Neil J. McLean
School of Psychology, The University of Western Australia
Acknowledgement: Jessica E. Tearne was supported by a University of Western Australia Completion Scholarship. Monique Robinson is supported by a NHMRC Early Career Fellowship. The authors acknowledge the funding and support of the Raine Medical Research Foundation, The University of Western Australia (UWA), the Telethon Kids Institute, the UWA Faculty of Medicine, Dentistry and Health Sciences, the Women and Infants Research Foundation, and Curtin University of Technology. We acknowledge the long term support and funding from the National Health and Medical Research Council (NHMRC) of Australia and the Raine Medical Research Foundation. The authors are extremely grateful to all the Raine Study participants and their families who took part in this study, as well as the Raine Study team for their cohort coordination and data collection.
Both younger and older parental age has been linked to mental health problems in offspring. There is a substantial literature relating young parenthood, particularly teenaged motherhood, to adverse mental health outcomes in young children (Do et al., 1998; Fergusson & Woodward, 1999; Harden et al., 2007; McGrath et al., 2014). Relative to offspring of older aged parents, offspring of teenaged mothers are at increased risk of mood disorders, internalizing problems (e.g., withdrawal, depression/anxiety, somatic symptoms), substance misuse, and juvenile crime (Fergusson & Woodward, 1999; Harden et al., 2007). In terms of psychiatric diagnoses, offspring of teenaged mothers have been found to have a 51% increased risk of having any psychiatric diagnosis, and offspring of teenaged fathers a 28% increased risk (McGrath et al., 2014).
In the case of older parental age and offspring mental health problems, the research has focused overwhelmingly on psychiatric diagnoses. There is now strong evidence that the children of older fathers are at heightened risk of schizophrenia and autism spectrum disorders (Hultman, Sandin, Levine, Lichtenstein, & Reichenberg, 2011; Miller et al., 2011), while the evidence for increased risk for bipolar disorder diagnosis is mixed with some studies suggesting a relationship (Frans et al., 2008; Menezes et al., 2010) and others finding no association (Buizer-Voskamp et al., 2011; McGrath et al., 2014). It has also been suggested that the effect of paternal age may be sexually dimorphic (Byrne, Agerbo, Ewald, Eaton, & Mortensen, 2003; Miller et al., 2011). In the case of maternal age, advanced maternal age has been linked to increased risk for autism spectrum disorders (Sandin et al., 2012). Another study found that older maternal age increases risk for bipolar disorder diagnosis in offspring (Menezes et al., 2010), whereas other studies do not support this relationship (Frans et al., 2008; McGrath et al., 2014).
There is less information on the relationship between parental age and other mood disorders such as depression and anxiety. One large scale study using data from a Dutch population registry found adult offspring of both younger (<20) and older (≥40) fathers had significantly increased odds of a major depressive disorder diagnosis (Buizer-Voskamp et al., 2011). Similarly, relative to offspring of 25- to 29-year-old parents, the adult offspring of teenaged mothers and fathers, as well as older fathers, have been found to have increased incidence of mood disorders (McGrath et al., 2014). Conversely, Fergusson and Woodward (1999) found a significant linear association between increasing maternal age and decreasing rates of anxious and depressive disorders (as per Diagnostic and Statistical Manual of Mental Disorders, 4th ed. [DSM–IV] criteria) in 18-year-old offspring. Other studies using data from the Western Australian Pregnancy Cohort (Raine) Study have indicated that maternal age is a significant prenatal predictor of risk for child behavior outcomes from age 2 to 14 (Tearne et al., 2014) and that there is a significant linear association between maternal age and risk for problem internalizing and externalizing behaviors in children from ages 2–17, whereas older maternal age is associated with decreased risk for child behavior problems (Tearne et al., 2015).
To our knowledge, there are no studies that examine the incidence of symptoms of depression and anxiety (as opposed to diagnosis with a major depressive or anxious disorder) as a function of parental age in young adults. Furthermore, parental age has not been examined in relation to stress in offspring as far as we are aware. Investigation of these issues is important because it is recognized that mental health issues may not always be limited to those who meet criteria for a psychiatric diagnosis. By limiting the focus of study to those who meet diagnostic criteria, the broader spectrum of psychological adjustment and distress is ignored. This study sought to examine the relationship between maternal and paternal age and depression, anxiety and stress symptoms, measured by the short form of the Depression Anxiety Stress Scales (DASS-21) in offspring in a population-based cohort of Western Australian young adults, and to build upon previous studies from the Raine cohort examining parental age and mental health outcomes in offspring by considering outcomes in young adults. Given findings that there may be a sexually dimorphic effect of parental age on offspring outcomes in terms of severe mental health outcomes, we also sought to determine whether sex modifies the relationship between parental age and a broader spectrum of mental health outcomes in offspring. In line with previous literature, it was hypothesized that offspring of teenaged mothers and fathers would be at increased risk of elevated DASS-21 scores. The existing findings relating to mood symptoms in offspring and older parents are mixed, and as such we sought to clarify what relationships, if any, existed between older parental age and depression, anxiety and stress in offspring.
Method Study Population
The Raine Study is a population-based prospective pregnancy cohort study. The methodology for the study has been described in detail elsewhere (Newnham, Evans, Michael, Stanley, & Landau, 1993). Briefly, 2900 pregnant women were recruited to the study between 16 and 20 weeks’ gestation through the public antenatal clinic at King Edward Memorial Hospital (KEMH) in Perth, Western Australia, or surrounding private practices between May 1989 and November 1991. The criteria for enrolment into the study were English language proficiency sufficient to understand the implications of participating in the study, an expectation that they would deliver at KEMH, and an intention to remain in Western Australia to facilitate follow-up of their child(ren). Ninety percent of eligible women agreed to take part. Participants provided data on psychosocial and sociodemographic characteristics at enrolment and again at 34 weeks’ gestation. A total of 2,868 live infants and their families have since undergone assessment at ages 1, 2, 3, 5, 8, 10, 14, 17, 20, and 23 years. Written parental and adolescent/young adult consent (14, 17, 20, and 23) was provided at each follow-up. It has been previously reported that the initial Raine sample overrepresented socially disadvantaged families, and that selective attrition of the sample over time led to a closer representation of those in the sample to the Western Australian population (Robinson et al., 2010). The protocols for the study were approved by the Human Research Ethics Committees at KEMH and the Princess Margaret Hospital for Children in Perth, Western Australia. Ethics approval for the 20-year follow-up was obtained from the University of Western Australia Human Research Ethics Committee.
Loss to Follow-up
Data collection for the 20-year follow-up took place between March 2010 and April 2012. There were 2,125 young adults eligible for follow-up at 20 years. Of the 1,565 (74%) who participated, 78% (n = 1,220) completed the DASS-21. Characteristics of those who completed the DASS-21 at follow-up compared with those from the original cohort who did not are presented in Table 1.
Characteristics of Participants and Nonparticipants From the Original Cohort
Mental Health Data
Anxiety, depression, and stress were assessed by using the short form of the Depression Anxiety Stress Scales (DASS-21; S. H. Lovibond & P. F. Lovibond, 1995b). The DASS-21 is a short form of the 42-item DASS (S. H. Lovibond & P. F. Lovibond, 1995b), with both scales found to have good reliability and validity in clinical and nonclinical samples (Antony, Bieling, Cox, Enns, & Swinson, 1998; Crawford & Henry, 2003; Henry & Crawford, 2005). The DASS comprises three 7-item scales measuring depression, anxiety, and stress. The depression scale assesses dysphoria, hopelessness, devaluation of life, self-depreciation, lack of interest/involvement, anhedonia, and inertia; the anxiety scale measures autonomic arousal, skeletal musculature effects, situational anxiety, and subjective experience of anxious affect; and the stress scale assesses difficulty relaxing, nervous arousal, being easily upset/agitated, irritable/overreactive, and impatient (S. H. Lovibond & P. F. Lovibond, 1995b). Participants were asked to rate the severity of each symptom during the past week on a 4-point scale ranging from 0 (“did not apply to me at all”) to 3 (“applied to me very much, or most of the time”). Scores were doubled as per the scoring instructions.
The depression and anxiety scales of the 42-item DASS show good convergent validity with the Beck Anxiety and Depression Inventories (Lovibond & Lovibond, 1995a). Several studies have suggested temporal stability of the DASS across time (Brown, Chorpita, Korotitsch, & Barlow, 1997; Cunningham, Brown, Brooks, & Page, 2013; Page, Hooke, & Morrison, 2007; Willemsen, Markey, Declercq, & Vanheule, 2011). A large-scale study has shown stability of symptoms as measured by the DASS over 3 to 8 years (Lovibond, 1998). Analyses specific to the DASS-21 have shown a quadripartite structure, which consisted of a general factor that the authors suggested reflected general psychological distress and orthogonal factors suggested to represent depression, anxiety, and stress (Henry & Crawford, 2005). While there is evidence for a common factor representing shared variance underlying the DASS scales, there is also strong evidence for specific factors underlying the depression, anxiety, and stress subscales. Furthermore, there is extensive evidence in the literature that anxiety and depression are not independent constructs (Clark & Watson, 1991), and thus evidence for shared variance underlying the DASS subscales provides support for the construct validity of the DASS. As a result, the three subscales were included as the outcome measures in the present study.
Predictor Variables
Parental age and date of birth were recorded at initial recruitment. Both maternal and paternal age in years at birth of the study child were calculated and modeled as continuous and categorical variables. In the case of parental age as a categorical variable, age was stratified into 5-year age groups (<20, 20–24, 25–29, 30–34, 35–39, ≥40 years of age), but for mothers the older two age categories were collapsed to form one (≥35) because of small numbers of women aged 40 and over in the sample. This categorization is often used in classification of population fertility data and broader epidemiological investigations (Australian Bureau of Statistics, 2010; Buizer-Voskamp et al., 2011). A maternal and paternal age of 25–29 years was set as the reference group in all analyses because the peak fertility rate for Australian women was in this age group at the time of recruitment to the Raine Study (Australian Bureau of Statistics, 2010).
Control Variables
We adjusted for several prenatal variables previously established as key predictors of mental health outcomes in the Raine cohort (Tearne et al., 2014). These variables included maternal education (12 or more years of education compared with 11 or fewer), maternal smoking during the first 18 weeks of pregnancy (no smoking compared with any smoking), maternal experience of stressful life events in the first 18 weeks of pregnancy (two or fewer compared with three or more), total family income as at 18 weeks of pregnancy (<24,000 AUD compared with ≥24,000 AUD, in accordance with the poverty line at the time of collection), and maternal diagnosis of gestational hypertension (no hypertension compared with any hypertension).
Statistical Analyses
We compared characteristics of participants who completed the DASS-21 at the 20-year follow-up with nonparticipants from the original cohort based on gender, race, maternal education, total family income at 18 weeks’ gestation, maternal smoking in the first 18 weeks of pregnancy, maternal experience of stressful life events in the first 18 weeks of pregnancy, gestational hypertension, and maternal and paternal age at birth of study child using χ2 tests. We examined the skewness of the DASS-21 scales and set skewness >1 as an indicator of suitability for nonparametric analysis. All subscales were found to be skewed.
Given the skewness of the DASS-21 subscale scores, we performed negative binomial regression analyses with a log link to investigate the association between maternal and paternal age and DASS-21 scores (depression, anxiety and stress subscale scores). These elicited a rate ratio (RR) which we interpreted as the proportional increase in DASS-21 scores compared with the reference category for the categorical models. Several studies report gender based differences in DASS scores (Crawford & Henry, 2003; Gomez, Summers, Summers, Wolf, & Summers, 2014). In particular, one study using a nonclinical sample found gender differences in depression and anxiety subscale scores and on total DASS scores (female scores significantly higher than male; Crawford & Henry, 2003). As such, preliminary analyses tested whether sex moderated the association between parental age and mental health outcomes. We initially ran models adjusting only for the age of the other parent, with the subsequent models adjusting for age and known confounding variables as detailed previously. Analyses were performed using IBM SPSS Statistics Version 22.
ResultsCharacteristics of participants and nonparticipants from the original cohort are presented in Table 1. Compared with those from the original cohort who did not take part in the current study (n = 1,648), those participants who took part in the current study (n = 1,220) were more likely to have older mothers, older fathers, and to have come from families with an income above the poverty line during pregnancy with the study child. Their mothers were more likely to have finished high school and were less likely to have smoked and experienced stressful life events during pregnancy with the study child. Boys from the original cohort were less likely than girls to participate at age 20. There were no differences based on race or gestational hypertension between participants and nonparticipants from the original cohort. Given the significant differences between the initial Raine population and those that completed the DASS at the 20-year follow-up, inverse probability weighting was used to standardize the sample and adjust for bias that may result from nonrandom attrition. Weights were created using the previously mentioned pregnancy variables, from which a probability of participation and the inverse of this probability were created. Applying these weights created a sample with approximately similar distribution to that of the original Raine cohort.
The internal consistency of the DASS-21 scales was moderate to high (Cronbach’s alpha; Total scale = .93; Depression scale = .89; Anxiety scale = .76; Stress scale = .86). Median and mean DASS-21 total and subscale scores are presented in Table 2. Mean and median scores in this sample were slightly higher than those reported in another nonclinical sample (Henry & Crawford, 2005). There were significant differences between male and female scores on all subscales of the DASS-21, with females scoring higher. Maternal and paternal age were moderately correlated with each other (r = .47, p > .01).
Median, Mean (SD) Depression Anxiety Stress Scale (DASS) Total and Subscales Scores for Females and Males
We initially tested whether there was an interaction between maternal age and offspring gender, and paternal age and offspring gender, and offspring outcomes. There was a significant interaction between maternal age and offspring gender, and paternal age and offspring gender for total DASS scores and all symptom scale scores. As such, all analyses were stratified based on gender (see supplementary information available online).
In the final multivariate models, where we adjusted for age of other parent and known confounders, a maternal age of 30–34 years was associated with significantly increased stress (RR = 1.27, p = .031) subscale scores in female offspring relative to the reference group (Table 3). A maternal age of 35 years and over was associated with increases in all subscale scores in female offspring (depression: RR = 1.51, p = .026; anxiety: RR = 1.51, p = .029; stress: RR = 1.36, p = .033). There was some evidence of an association between a paternal age of 30–34 years and decreased stress subscale scores in female offspring (RR = .80, p = .045). No other paternal ages were associated with significantly different risk for DASS subscale scores in female offspring. The relationships between maternal and paternal age and DASS subscale scores are presented in Figures 1–3. There was some evidence that young maternal age was associated with decreased stress scale scores in male offspring (RR = .63, p = .04). No other maternal nor paternal age groups were associated with significantly different DASS subscale scores (Table 4).
Adjusted Analyses Estimating the Effect of Maternal and Paternal Age on Total Depression Anxiety Stress Scale (DASS) Scores and Depression, Anxiety, and Stress Subscale Scores in Girls
Figure 1. Depression subscale. See the online article for the color version of this figure.
Figure 2. Anxiety subscale. See the online article for the color version of this figure.
Figure 3. Stress subscale. See the online article for the color version of this figure.
Adjusted Analyses Estimating the Effect of Maternal and Paternal Age on Total Depression Anxiety Stress Scale (DASS) Scores and Depression, Anxiety, and Stress Subscale Scores in Boys
Figure 1. Depression subscale. See the online article for the color version of this figure.
Figure 2. Anxiety subscale. See the online article for the color version of this figure.
Figure 3. Stress subscale. See the online article for the color version of this figure.
DiscussionOur results suggest that older maternal age is related to an increased risk of depression, anxiety, and stress symptoms in young adult females. Paternal age was not found to be related to risk in females, and neither maternal nor paternal age predicted risk of these symptoms in young adult males. These relationships persisted after adjustment for a number of factors known to influence mental health in offspring.
Our results differ from other studies suggesting that older paternal age is linked to increased incidence of mood disorders (Buizer-Voskamp et al., 2011; McGrath et al., 2014) and with a larger body of literature suggesting older paternal age is associated with a range of other adverse psychiatric outcomes in offspring (Hultman et al., 2011; McGrath et al., 2014; Miller et al., 2011). A key difference is that our study examined self-reported symptoms of depression, anxiety, and stress rather than clinical diagnoses. It is plausible that the risk factors for psychological adjustment and distress differ from those risk factors identified for more severe psychiatric outcomes. The results of our study suggest that when moving beyond diagnosis to consider a broader spectrum of psychological distress and adjustment in offspring, paternal age is not an important factor of influence, at least in this sample when using the DASS-21 as an outcome variable. This is an important finding when placed in the context of the existing literature, because it suggests that father’s age may have a differential impact on different types of psychiatric distress/illness and may not be relevant for all outcomes. It is plausible that at the level of distress, rather than disorder, associations with parental age may stem from environmental factors, such as interactions with the parent, rather than biology. It may be the case that the significance of maternal and not paternal age as predictors of offspring outcomes may reflect an imbalance in key relationships in the caregiving of the child, such that maternal age exerts a greater influence because mothers may have played a greater caregiving role. In the few existing studies examining maternal age and mood disorders in offspring, older maternal age has been found to have no significant association with offspring outcome in two studies (Buizer-Voskamp et al., 2011; McGrath et al., 2014) and was associated with decreased risk for depressive and anxious disorders in 18-year-old offspring (Fergusson & Woodward, 1999), and decreased risk for internalizing disorders across childhood (Tearne et al., 2015). Our study suggests that maternal age is implicated in the subsequent experience of symptoms of depression, anxiety, and stress in female young adult offspring. This is somewhat different from the results presented in the aforementioned studies, although our findings are consistent with a study finding older maternal age may be associated with increased risk for bipolar affective disorder (Menezes et al., 2010). This finding is also broadly consistent with a number of studies suggesting advanced maternal age is associated with increased risk for autism spectrum disorders in offspring (Croen, Najjar, Fireman, & Grether, 2007; Durkin et al., 2008; Grether, Anderson, Croen, Smith, & Windham, 2009; King, Fountain, Dakhlallah, & Bearman, 2009; Parner et al., 2012; Sandin et al., 2012)
Future research should attend to uncovering potential mechanisms underlying the relationship between maternal age and depression, anxiety and stress symptoms in female offspring. It is possible that it is not so much age at pregnancy that underpins the relationship between maternal age and symptoms in female offspring, but age of the mother at follow-up assessment (which is an indirect effect of age at pregnancy). One possible hypothesis is difficulties in the mother–daughter relationship because of a large age difference between the two parties. The “older mothers” in our sample were 50–54 and 55 and over when their offspring were 20 years of age. It may be that a 30 or more year age difference between mother and daughter leads to a significant difference in the value systems held by each, as well as generational differences that may cause tension in the relationship, particularly during the transition period of young adulthood, leading to stress, worry, and sadness in the child. The increased incidence of depression, anxiety, and stress symptoms may reflect a stressful period in the lives of both mother and daughter. Another example of possible age-related differences in mother–daughter relationships is the impact of age-related health changes and problems in mothers. The median age at which women in Australia go through menopause is around 51 years of age (Do et al., 1998). Statistics from the Centers for Disease Control and Prevention suggest that once women enter their fifties, the leading causes of mortality are various cancers, heart disease, and chronic respiratory conditions (Centers for Disease Control and Prevention, 2010). It has been found that levels of emotional distress and behavioral problems escalate in adolescents and young adults with an immediate family member with a cancer diagnosis (Sahler et al., 1994), and another study suggested that adolescent female offspring are most negatively affected by a parent’s diagnosis with serious illness (Osborn, 2007). Thus, the higher risk of depression, anxiety, and stress in offspring of women in their fifties may be because of health-related stress and concern within the family. It may be that significant life changes are occurring in parallel in mothers and daughters, which may influence emotion dysregulation in offspring.
Another possible explanation for our results is that the relationship between advancing maternal age and offspring mental health outcomes observed in this study may be because of unmeasured confounding. Examining the relationship between maternal age and offspring mental health outcomes is complex, owing to the great number of variables associated with older motherhood that may also exert an influence on offspring outcomes. The statistical position taken in this study was that variables measured at the same time as the key outcome variables (i.e., prenatal variables) were considered as potential confounders, and our large sample size allowed for an exhaustive list of control variables to eliminate, as far as possible, confounding. However, there are myriad other factors that may influence the mental health of offspring. Recent studies in the area using quasi-experimental designs to control for environmental and genetic influences that vary within families using sibling-comparison analyses have yielded interesting findings. One study indicated that environmental factors associated with maternal age at childbirth which also vary within families are implicated in the incidence of delinquent behaviors in offspring (D’Onofrio et al., 2009), while another indicated that controlling for variables shared within families strengthened the association between advanced paternal age and various indices of psychopathology, consistent with a causal hypothesis (D’Onofrio et al., 2014). Although beyond this scope of this study, future research designs controlling for factors shared within families may leave researchers better placed to identify the specific factors, be they genetic, environmental, or both, that influence offspring behavior. This would allow us to better specify how maternal age may influence depression, anxiety and stress symptoms in young adult offspring, and why this relationship may be specific to female offspring.
There are a number of strengths associated with this study. Our prospectively collected data are drawn from a large cohort study, allowing us the opportunity for a comprehensive assessment of the impact of parental age on anxiety, stress, and depression symptoms in offspring in a nonclinical population. However, our findings must be interpreted in the context of a number of limitations. First, a limitation is our use of self-report data. Self-report measures have been validated as a valid means of assessing depression, anxiety, and stress (Antony et al., 1998). While we did not set out to measure clinical levels of distress, but rather more general symptoms of distress in our sample, we cannot rule out the possibility of over- or underreporting. A second limitation is the relatively small numbers of parents in the oldest (aged 40 and over) and youngest (19 and under) age groups at childbirth in our sample (2.3% and 9.7%, respectively). This may have impacted upon the strength of the influence of parental age upon offspring in these categories. Furthermore, the DASS-21 data measure symptoms over the past week. While a study using the longer version of the DASS scale has shown stability of each of the syndromes over substantial periods of time (3 to 8 years), future research could look to investigate the stability of symptoms over time in the Raine and similar cohorts. Another consideration was that it was not possible to differentiate between parental age at birth of first child versus birth of the study child. It has been suggested it may be parental age at birth of first child, not birth of the individual child, which predicts mental health outcomes in offspring (Petersen, 2011). An investigation of this type was beyond the scope of this study but is a worthy focus of future research. Finally, we controlled for a comprehensive range of other prenatal variables known to impact upon mental health in offspring, but this list is not exhaustive and does not take into account the myriad other influences on mental health across the life span. For example, family structure was not accounted for in this study. Many variables of interest, such as maternal mental health at follow-up, were not available to us. These variables may impact upon the relationships observed in the data, and further research is necessary to evaluate their impact. Despite these limitations, our data provide new insights into the impact of parental age on general symptoms of anxiety, depression, and stress in young adult offspring.
ConclusionsWe found that a maternal age of 30–34 years was associated with significant increases in total DASS-21 scores in female offspring, and a maternal age of 35 years and over was associated with significant increases in total and subscale DASS-21 scores. Paternal age was not found to be associated with offspring depression, anxiety, and stress. It appears that when examining a broad spectrum of psychological adjustment, the relationships between parental age and offspring symptomatology differ from those in the literature on parental age and severe psychiatric outcomes. We suggest that maternal age when the young adult is assessed may be as important as considering age at pregnancy.
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Submitted: January 27, 2015 Revised: September 9, 2015 Accepted: September 10, 2015
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Source: Journal of Abnormal Psychology. Vol. 125. (1), Jan, 2016 pp. 1-10)
Accession Number: 2015-51755-001
Digital Object Identifier: 10.1037/abn0000119
Record: 36- Title:
- Onset of alcohol or substance use disorders following treatment for adolescent depression.
- Authors:
- Curry, John. Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, US, john.curry@duke.edu
Silva, Susan. Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, US
Rohde, Paul. Oregon Research Institute, Eugene, OR, US
Ginsburg, Golda. Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD, US
Kennard, Betsy. Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, US
Kratochvil, Christopher. Department of Psychiatry, University of Nebraska Medical Center, NE, US
Simons, Anne. Department of Psychology, University of Oregon, Eugene, OR, US
Kirchner, Jerry. Duke Clinical Research Institute, NC, US
May, Diane. Department of Psychiatry, University of Nebraska Medical Center, NE, US
Mayes, Taryn. Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, US
Feeny, Norah. Department of Psychology, Case Western Reserve University, Cleveland, OH, US
Albano, Anne Marie. Department of Psychiatry, Columbia University Medical Center, New York, NY, US
Lavanier, Sarah. Division of Psychiatry, Cincinnati Children’s Medical Center, Cincinnati, OH, US
Reinecke, Mark. Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, IL, US
Jacobs, Rachel. Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, IL, US
Becker-Weidman, Emily. Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, IL, US
Weller, Elizabeth. Department of Child and Adolescent Psychiatry and Behavioral Science, Children’s Hospital of Philadelphia, Philadelphia, PA, US
Emslie, Graham. Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, US
Walkup, John. Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD, US
Kastelic, Elizabeth. Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD, US
Burns, Barbara. Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, US
Wells, Karen. Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, US
March, John. Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, US - Address:
- Curry, John, Duke Child and Family Study Center, 2608 Erwin Road, Suite 300, Durham, NC, US, 27705, john.curry@duke.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 80(2), Apr, 2012. pp. 299-312.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 14
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- adolescents, alcohol use disorders, major depression, substance use disorders
- Abstract:
- Objective: This study tested whether positive response to short-term treatment for adolescent major depressive disorder (MDD) would have the secondary benefit of preventing subsequent alcohol use disorders (AUD) or substance use disorders (SUD). Method: For 5 years, we followed 192 adolescents (56.2% female; 20.8% minority) who had participated in the Treatment for Adolescents with Depression Study (TADS; TADS Team, 2004) and who had no prior diagnoses of AUD or SUD. TADS initial treatments were cognitive behavior therapy (CBT), fluoxetine alone (FLX), the combination of CBT and FLX (COMB), or clinical management with pill placebo (PBO). We used both the original TADS treatment response rating and a more restrictive symptom count rating. During follow-up, diagnostic interviews were completed at 6- or 12-month intervals to assess onset of AUD or SUD as well as MDD recovery and recurrence. Results: Achieving a positive response to MDD treatment was unrelated to subsequent AUD but predicted a lower rate of subsequent SUD, regardless of the measure of positive response (11.65% vs. 24.72%, or 10.0% vs. 24.5%, respectively). Type of initial MDD treatment was not related to either outcome. Prior to depression treatment, greater involvement with alcohol or drugs predicted later AUD or SUD, as did older age (for AUD) and more comorbid disorders (for SUD). Among those with recurrent MDD and AUD, AUD preceded MDD recurrence in 24 of 25 cases. Conclusion: Effective short-term adolescent depression treatment significantly reduces the rate of subsequent SUD but not AUD. Alcohol or drug use should be assessed prior to adolescent MDD treatment and monitored even after MDD recovery. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Adolescent Psychopathology; *Alcoholism; *Drug Abuse; *Major Depression; *Onset (Disorders); Substance Use Disorder
- Medical Subject Headings (MeSH):
- Adolescent; Antidepressive Agents; Cognitive Therapy; Combined Modality Therapy; Depressive Disorder, Major; Female; Fluoxetine; Follow-Up Studies; Humans; Male; Substance-Related Disorders; Treatment Outcome
- PsycINFO Classification:
- Psychological & Physical Disorders (3200)
- Population:
- Human
Male
Female - Age Group:
- Adolescence (13-17 yrs)
- Tests & Measures:
- Survey of Outcomes Following Treatment for Adolescent Depression
Clinical Global Impressions–Improvement scale
Suicide Ideation Questionnaire–Junior High Version
Children's Depression Rating Scale--Revised DOI: 10.1037/t55280-000
Personal Experience Screening Questionnaire DOI: 10.1037/t15632-000
Reynolds Adolescent Depression Scale
Children's Global Assessment Scale
Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version DOI: 10.1037/t03988-000 - Grant Sponsorship:
- Sponsor: National Institute of Mental Health
Grant Number: MH70494
Recipients: Curry, John - Methodology:
- Empirical Study; Followup Study; Longitudinal Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Jan 16, 2012; Accepted: Nov 16, 2011; Revised: Nov 1, 2011; First Submitted: Jan 20, 2011
- Release Date:
- 20120116
- Correction Date:
- 20170413
- Copyright:
- American Psychological Association. 2012
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0026929
- PMID:
- 22250853
- Accession Number:
- 2012-00540-001
- Number of Citations in Source:
- 62
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-00540-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-00540-001&site=ehost-live">Onset of alcohol or substance use disorders following treatment for adolescent depression.</A>
- Database:
- PsycINFO
Onset of Alcohol or Substance Use Disorders Following Treatment for Adolescent Depression
By: John Curry
Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, and Duke Clinical Research Institute, Durham, North Carolina;
Susan Silva
Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, and Duke Clinical Research Institute, Durham, North Carolina
Paul Rohde
Oregon Research Institute, Eugene, Oregon;
Department of Psychology, University of Oregon
Golda Ginsburg
Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University School of Medicine
Betsy Kennard
Department of Psychiatry, University of Texas Southwestern Medical Center at Dallas
Christopher Kratochvil
Department of Psychiatry, University of Nebraska Medical Center
Anne Simons
Department of Psychology, University of Oregon
Jerry Kirchner
Duke Clinical Research Institute
Diane May
Department of Psychiatry, University of Nebraska Medical Center
Taryn Mayes
Department of Psychiatry, University of Texas Southwestern Medical Center at Dallas
Norah Feeny
Department of Psychology, Case Western Reserve University
Anne Marie Albano
Department of Psychiatry, Columbia University Medical Center
Sarah Lavanier
Division of Psychiatry, Cincinnati Children's Medical Center, Cincinnati, Ohio
Mark Reinecke
Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine
Rachel Jacobs
Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine
Emily Becker-Weidman
Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine
Elizabeth Weller
Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
Graham Emslie
Department of Psychiatry, University of Texas Southwestern Medical Center at Dallas
John Walkup
Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University School of Medicine
Elizabeth Kastelic
Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University School of Medicine
Barbara Burns
Department of Psychiatry and Behavioral Sciences, Duke University Medical Center
Karen Wells
Department of Psychiatry and Behavioral Sciences, Duke University Medical Center
John March
Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, and Duke Clinical Research Institute
Acknowledgement: Elizabeth Weller is now deceased. We gratefully acknowledge her many contributions to the treatment of depressed adolescents.
Susan Silva is now at the Duke School of Nursing. Anne Simons is now at the Department of Psychology, University of Notre Dame. Sarah Lavanier is now at the Lindner Center for Hope, Mason, Ohio. Rachel Jacobs is now at the Department of Psychiatry, Columbia University Medical Center. Emily Becker-Weidman is now at the New York University Child Study Center. John Walkup is now at the Department of Psychiatry, Weill Cornell Medical Center.
Christopher Kratochvil, Graham Emslie, and John March have been consultants to—and Christopher Kratochvil and Graham Emslie have had research support from—Eli Lilly, which manufactures fluoxetine.
This research was supported by National Institute of Mental Health (NIMH) Grant MH70494 to John Curry. We gratefully acknowledge the contributions of Benedetto Vitiello, who coordinated administration of this project at the NIMH. We also thank the study participants, the site recruitment staff (including Margaret Price, Stephanie Frank, and Sue Babb), and the site management coordinator (Kathleen Girardin).
Alcohol or other substance use disorders (AOSUDs), including psychoactive substance abuse or dependence, are among the most common adolescent psychiatric disorders. Point prevalence rates are approximately 2%–3%, with lifetime prevalence rates reaching 12.2% by 16 years of age (Costello, Mustillo, Erkanli, Keeler, & Angold, 2003; Lewinsohn, Hops, Roberts, Seeley, & Andrews, 1993). AOSUDs increase over the adolescent age range (Costello et al., 2003), frequently follow a chronic or relapsing course (Kaminer, Burleson, & Burke, 2008), and are associated with multiple negative correlates or outcomes, including criminal justice involvement, high-risk sexual behavior, and suicide attempts (Tims et al., 2002; Wu et al., 2004). Because of their prevalence and negative functional impact, it is critical to prevent development of these disorders in vulnerable adolescents.
Adolescents with AOSUDs frequently have other disorders, such as conduct disorder or depression. Such disorders may develop earlier than, and constitute risk factors for, subsequent alcohol or drug disorders (Armstrong & Costello, 2002). Therefore, to the extent that treatments for these earlier disorders are effective, they might also mitigate the risk for development of subsequent AOSUDs (Kendall & Kessler, 2002). In the present study, we investigate whether effective treatment for adolescent major depressive disorder (MDD) exerts such a secondary benefit (Glantz et al., 2009) by investigating subsequent onset of AOSUDs among participants in the multisite Treatment for Adolescents with Depression Study (TADS; TADS Team, 2004).
Depressive symptoms in adolescence have been associated with subsequent increases in alcohol or drug use or related problems in several studies. However, studies vary in whether their focus is on alcohol or other substances, and findings are not entirely consistent, appearing to vary by gender and age. In longitudinal studies including both genders, Stice, Barrera, and Chassin (1998) found that depressed and anxious symptoms during adolescence predicted alcohol-related problems 1 year later, and Chen, Anthony, and Crum (1999) found that childhood or early adolescent depressive symptoms predicted early- to mid-adolescent alcohol-related problems. In the Dunedin Longitudinal Study, Henry et al. (1993) found that early adolescent depressive symptoms predicted mid-adolescent drug problems (multiple substance use), but only for boys. Similarly, in the Great Smokey Mountains Study, the effects of depression on substance use were stronger for boys than for girls: Boys with depressive symptoms reported them prior to the onset of cannabis use, abuse, or dependence (Costello, Erkanli, Federman, & Angold, 1999). Finally, Marmorstein (2009) found that depressive symptoms in early male adolescents, but not in female adolescents, predicted faster growth in alcohol-related problems through adolescence.
Two studies that failed to find a link between adolescent depressive symptoms and subsequent alcohol or drug problems measured late adolescent or early adult outcomes. Chassin, Pitts, DeLucia, and Todd (1999) found no effect of internalizing symptoms during adolescence on young adult alcohol use disorders (AUD) in a high-risk sample; and in the Dunedin Longitudinal Study, depressive symptoms at 15 years of age did not predict increased cannabis use at 18 years of age (McGee, Williams, Poulton, & Moffitt, 2000).
Even in single gender longitudinal studies, there is variability in the link between depression and subsequent alcohol versus drug outcomes. Two recent reports from a study of adolescent girls (Measelle, Stice, & Hogansen, 2006; Measelle, Stice, & Springer, 2006) indicated that negative emotionality predicted onset of alcohol or substance abuse, whereas depressive symptoms predicted worsening in substance abuse. Taken together, these findings suggest that the link between depressive symptoms and subsequent alcohol or substance use, problems, or disorders may vary by age, gender, and whether alcohol or other substance-related outcomes are assessed.
Compared to depressive symptoms, fewer studies have investigated the potential link between diagnosed adolescent depressive disorders and subsequent alcohol or other substance-related problems. A large prospective study of Finnish twins concluded that depressive disorders at 14 years of age predicted more frequent drug and alcohol use and recurrent intoxication by 17.5 years of age (Sihvola et al., 2008). Rohde, Lewinsohn, and Seeley (1996) found that among adolescents with both depressive and alcohol disorders, there was not a consistent temporal pattern of onset, but depression occurred first in a substantial number of cases (58%). On the other hand, some studies have found that adolescent depressive disorders do not predict subsequent AOSUDs, or that earlier drug or alcohol use predicts depressive disorders (D. W. Brook, Brook, Zhang, Cohen, & Whiteman, 2002; J. S. Brook, Cohen, & Brook, 1998).
Two considerations may clarify the nature of the link between adolescent depressive disorders and AOSUDs. First, any linkage may be bidirectional (Costello et al., 1999; Swendsen & Merikangas, 2000). If so, a test of the potential secondary preventive benefits of effective depression treatment on subsequent AOSUDs should be conducted with a depressed sample free of preexisting AOSUDs. Second, the link between depressive disorders and subsequent AOSUDs may be indirect, that is, attributable to other factors. Fergusson and Woodward (2002) found that adolescents who developed MDD between 14 and 16 years of age were significantly more likely than those who did not to develop both recurrent MDD and an AUD by 21 years of age. However, whereas the link between initial and subsequent MDD episodes was direct, the link between adolescent MDD and subsequent AUD was attributable to other factors, including early drinking and peer influence. Thus, any elevated risk of AOSUDs associated with adolescent MDD may be small or non-significant when assessed in the context of other factors (Measelle, Stice, & Springer, 2006). Therefore, when testing whether effective treatment of adolescent MDD has a secondary benefit of preventing subsequent AOSUDs, it is important to include additional predictors of AOSUDs that were present before treatment.
Taking these two considerations into account, in this study we investigated the preventive effects of successful depression treatment on subsequent AUD or other substance use disorders (SUD) in a sample with no preexisting AUD or SUD. We investigated AUD and SUD separately for several reasons. First, as noted above, previous studies have found variable results, depending on whether alcohol or other substance-related problems were assessed. Second, the trajectories of AUD and SUD differ across the age range under investigation. AUD is slightly more prevalent than SUD in early adolescence (Chassin, Ritter, Trim, & King, 2003), but it becomes much more prevalent by 20 years of age (Cohen et al., 1993) and has a substantially higher lifetime prevalence among adults (Kessler et al., 2005). Third, among adolescent psychiatric patients, the correlates of alcohol abuse and of other substance abuse are not identical (Becker & Guilo, 2006), and among college students, alcohol abuse is associated with major depression, but other substance abuse is associated both with major depression and with other comorbid diagnoses (Deykin, Levy, & Wells, 1987).
We took into account several possible additional predictors of AUD and SUD evident before depression treatment to ensure that any secondary benefit of successful depression treatment on subsequent AUD or SUD could not be accounted for by these other predictors. The potential predictors included demographic variables (age, gender, and ethnicity), comorbid disorders, and pretreatment use of alcohol or drugs. Demographic variables are important to consider, not only because previous studies have found age and gender differences in the linkage between depression and substance abuse but also because alcohol and drug use are more prevalent in older adolescents, are more prevalent in male adolescents, and vary by ethnic group (Substance Abuse and Mental Health Services Administration [SAMHSA], 2010). We included comorbid disorders because a large percentage of adolescents with MDD present with additional comorbid disorders (Kovacs, 1996), and the comorbid disorders, such as anxiety or disruptive behavior disorders, may be the source of risk for subsequent AUD or SUD (Armstrong & Costello, 2002). Lastly, use of alcohol or drugs prior to treatment for MDD must be considered. Costello et al. (1999) found that first alcohol use preceded diagnosed AUD by approximately 6 years, with a comparable period of about 3 years between first cannabis use and a diagnosable SUD. It may be that depressed adolescents who are already involved in alcohol or drug use at the time of depression treatment have greater risk for subsequent AUD or SUD than those who are not.
Finally, we took into account the course of MDD following treatment, because this may influence development of AUD or SUD. The great majority of treated adolescents recover from their index MDD episode within 1–2 years, but rates of recurrent MDD across community and clinical samples range from 40% to 70% (Birmaher et al., 2000). In a previous report, we found that 88.3% of TADS adolescents recovered within 2 years (96.4% within 5 years) but that 46.6% of recovered adolescents experienced recurrent MDD within 5 years (Curry et al., 2011). Chronic or recurrent depression may increase the risk of AUD or SUD. Among adults treated for alcohol or drug dependence, an earlier lifetime history of MDD lowered the likelihood of successful drug or alcohol treatment, and MDD during a period of sustained alcohol or drug abstinence increased the risk of relapse (Hasin et al., 2002). In adolescents, depression is associated with more severe SUD and higher risk for SUD relapse (McCarthy, Tomlinson, Anderson, Marlatt, & Brown, 2005; Riggs, Baker, Mikulich, Young, & Crowley, 1995); in turn, alcohol or substance abuse is associated with longer episodes of depression in girls (King et al., 1996). None of these findings directly demonstrate that chronic or recurrent MDD raises the risk of AUD or SUD onset, but they suggest that persistent/ongoing MDD complicates efforts to avoid or achieve sustained remission from AUD or SUD. In the present study, we explored whether chronic or recurrent MDD among treated adolescents increased the risk of AUD or SUD onset.
In summary, we tested the hypothesis that, among depressed adolescents with no history of AUD or SUD, effective depression treatment would have the secondary benefit of preventing subsequent AUD or SUD. As noted by Kendall and Kessler (2002), it is not possible to compare treated versus untreated depressed adolescents, because withholding treatment would be unethical. However, it is possible to compare more effective versus less effective treatment of MDD. Indeed, Kendall, Safford, Flannery-Schroeder, and Webb (2004) showed that effective treatment of youth anxiety disorders lowered risk of subsequent substance use problems. Thus, we compared onset of AUD and SUD among TADS adolescents who successfully responded to acute depression treatment compared to non-responders.
We supplemented our primary analyses with secondary analyses to investigate whether the receipt of a specific type of acute depression treatment or the achievement of response to a specific acute treatment was associated with lower risk for subsequent AUD or SUD. In TADS, fluoxetine alone (FLX) led to a greater rate of short-term response than did cognitive behavior therapy (CBT), and the combination of CBT and FLX (COMB) led to the highest rate of positive short-term treatment response (TADS Team, 2004). On the other hand, CBT, alone or as part of COMB, was a skills-based intervention and included some skills that are also embedded in effective substance abuse prevention programs (e.g., goal-setting, problem-solving, social skills; Lochman & Wells, 2002). We had an insufficient basis in prior research to justify a priori hypotheses for these analyses, but it was possible that faster response (through COMB or FLX) or skills acquisition (through COMB or CBT) might be associated with more favorable subsequent AUD or SUD outcomes.
Method Relation of TADS to the Present Study
TADS compared CBT, FLX, and COMB to one another over the course of short-term (12 weeks), continuation (6 weeks), and maintenance (18 weeks) stages of treatment. During the first stage, the three active treatments were also compared to clinical management with a pill placebo (PBO). At Week 12, the medication blind was broken, and PBO non-responders were offered their TADS treatment of choice. After all three treatment stages (Week 36), adolescents were followed openly for 1 year (TADS Team, 2009).
The present study, Survey of Outcomes Following Treatment for Adolescent Depression (SOFTAD), was an open follow-up extending an additional 3.5 years. The total TADS–SOFTAD time period spanned 63 months (21 months of TADS and 42 months of SOFTAD), with diagnostic interviews administered at baseline and then at the following months after baseline: 3, 9, 15, 21 (end of TADS), 27, 33, 39, 51, 63.
The design, sample characteristics, and outcomes of TADS have been described in previous publications (TADS Team, 2004, 2007, 2009). TADS participants were 439 adolescents from 13 sites with moderate-to-severe, non-psychotic MDD. At the end of short-term treatment, positive response was defined as an independent evaluator rating of 1 (very much improved) or 2 (much improved) on the 7-point Clinical Global Impressions–Improvement scale (CGI-I; Guy, 1976). Adolescents rated with a score of 3 (minimally improved) or higher (no change or worsening) were categorized as non-responders.
Participants in SOFTAD were recruited from all 439 adolescents in TADS, regardless of compliance with treatment or assessments, treatment response, or time since TADS baseline, provided this was no greater than 63 months. TADS recruitment began in Spring 2000 and ended in Summer 2003. SOFTAD recruitment and assessments began in Spring 2004 and concluded in Winter 2008. Recruitment involved (a) recontacting TADS early completers and dropouts, and (b) after Spring 2004, asking adolescents and parents completing TADS to participate in SOFTAD. Written informed consent and, for minors, assent were obtained. The Duke University Health System Institutional Review Board (IRB) and each site IRB approved this study.
The initial SOFTAD assessment optimally occurred 27 months after TADS baseline. SOFTAD participants who were enrolled at that juncture could complete seven assessments at 6-month intervals, of which five included the diagnostic interviews that were used in the present analyses. Some participants, however, were not recruited until after 27 months, and their SOFTAD enrollment visit was the assessment that corresponded to their point of entry.
Sample Description
The total SOFTAD sample included 196 adolescents, recruited at 12 of the 13 TADS sites. Four SOFTAD participants were excluded from the present study because of AUD or SUD diagnosed at or before the end of TADS short-term treatment (Week 12). Thus, the sample for the present study consisted of 192 adolescents (84 male adolescents and 108 female adolescents; 43.7% of the 439 youths randomized to TADS treatments), with an average age at entry into TADS of 14.9 years (SD = 1.5 years). Their age at the end of SOFTAD ranged from 17 to 23 years, with a mean of 20.1 years (SD = 1.5 years). Table 1 includes demographic and clinical characteristics of the present sample at the time they entered TADS. The sample was 79% Caucasian, 9% Latino, 8% African American, and 4% other ethnicity. Ninety percent of the sample had been in their first episode of MDD at entry into TADS. At intake into TADS, they had been moderately to severely depressed, as indicated on the Children's Depression Rating Scale–Revised (CDRS-R; Poznanski & Mokros, 1996; sample raw score M = 59.4, SD = 10.3). Functional impairment was also in the moderate range on the 100-point Children's Global Assessment Scale (CGAS; Shaffer et al., 1983; sample M = 50.3, SD = 7.8). Forty-one of these adolescents (21%) had a comorbid disruptive behavior disorder, and 44 (23%) had a comorbid anxiety disorder.
TADS Baseline Characteristics of Current Study Participants and Non-Participants
The participants' point of entry into SOFTAD, in months since TADS baseline, was as follows: Month 27 (33%), Month 33 (22%), Month 39 (14%), Month 45 (11%), Month 51 (10%), Month 57 (8%), and Month 63 (2%). Of seven possible SOFTAD assessments, the modal number of completed assessments was 5, with a mean of 3.5 (SD = 1.5).
Criterion Measures
Diagnoses
To establish diagnoses, including those of AUD and SUD, the Schedule for Affective Disorders and Schizophrenia for School-Age Children–Present and Lifetime Version (K-SADS-PL; Kaufman et al., 1997) was administered at five of the seven SOFTAD assessment points. (The Month 45 and Month 57 assessment points included only self-report scales.) The K-SADS-PL had been used in TADS and, thus, was familiar to all participants. It was used to assess mood, anxiety, disruptive behavior, eating, substance use, psychotic, and tic disorders using Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; DSM–IV–TR; American Psychiatric Association, 2000) criteria. This interview has high concurrent validity, interrater, and test–retest reliability (Kaufman et al., 1997; TADS Team, 2004). At each K-SADS-PL administration, inquiry was made about symptoms and episodes of any disorder since the last TADS or SOFTAD assessment that the participant had completed and also about current symptoms of MDD, AUD, or SUD. The K-SADS-PL is typically administered to both the adolescent and a parent. Interview administration was adapted for SOFTAD, as participants were transitioning into young adulthood: The participant was always interviewed; the parent was interviewed if the participant was still living at home. This modification is consistent with other adaptations of the K-SADS for circumstances in which parental involvement is not feasible (Lewinsohn et al., 1993)
The K-SADS-PL includes an initial screen interview, with supplements for each disorder. The supplements are administered only if the screen indicates the possibility of the disorder. For AUD, the screen interview includes questions about quantity (three or more drinks in a day) and frequency of drinking (three or more days a week), and about whether significant others have expressed concern about the participant's drinking. If any item is answered positively, the supplement is administered. For SUD, the screen includes a list of possible drugs of abuse (cannabis, stimulants, anxiolytics, sedatives, cocaine, opioids, phencyclidine, hallucinogens, solvents, inhalants, ecstasy, and prescription drugs), and the participant is asked whether he or she has used any of these in the past 6 months. If non-prescribed use has occurred more than once a month, the supplement is administered. Supplement questions are anchored to DSM–IV–TR symptoms of abuse or dependence.
Episodes of MDD, AUD, or SUD
When the K-SADS-PL indicated that the participant met criteria for MDD, AUD, or SUD (other than nicotine), at any point since the last interview, the interviewer inquired about onset and, if relevant, offset of the episode. Onset was estimated as the month when the adolescent met all criteria for a disorder episode. Offset was estimated as the month when the adolescent had no remaining clinically significant symptoms of the disorder.
In a previous report (Curry et al., 2011), we focused on recovery from the TADS index episode of MDD and on recurrent MDD. Recurrent MDD was defined as a new episode following at least 8 weeks of no MDD symptoms. In this study, we focused on the emergence of episodes of AUD or SUD after short-term depression treatment, defined as those diagnosed after the TADS Week 12 interview. We also investigated the association between recurrent episodes of MDD and the onset of AUD or SUD.
Interviewer training and monitoring
SOFTAD evaluators met the same criteria as those of TADS evaluators (master's or doctoral degree in a mental health profession with previous experience administering research diagnostic interviews). Evaluators completed these steps for certification: (1) a videoconference training session; (2) a knowledge test passed with 80% correct answers; (3) rating a videotaped standard patient interview provided by the coordinating center, with 80% agreement on the full MDD, AUD, and SUD DSM–IV–TR criterion sets, agreement on these diagnoses, and agreement on other classes of disorders (e.g., anxiety disorder); and (4) completion and rating of an audiotaped site-based interview with an adolescent, subsequently rated at the coordinating center with acceptable reliability using the same criteria as in Step 3.
Following certification, evaluators participated in monthly conference calls to review interviews. Each evaluator was required during their 2nd and 3rd year in the project to reliably rate a patient interview provided by the coordinating center. On these recertification interviews (n = 24), there was complete agreement between evaluators and coordinating center raters on diagnosis of MDD “since the last interview” (k = 1.00) and there was 96% agreement on “current” MDD (k = .92); 91% of evaluator ratings for each time frame exceeded the 80% agreement level on DSM–IV–TR diagnostic criteria sets. For AUD, there was complete agreement for the diagnosis both “since the last interview” and “current episode” (k = 1.00). On the DSM–IV–TR criteria sets, 92% of ratings for “since last interview” and 90% for “current episode” exceeded 80% agreement. For SUD, there was 92% agreement on the diagnosis at each time frame (k = .82). For each time frame, 83% of evaluator ratings exceeded the 80% agreement level on the DSM–IV–TR criteria sets.
TADS Baseline Measures
For purposes of sample description or as potential predictors of subsequent AUD or SUD, the following variables were assessed at TADS baseline:
Age, race/ethnicity, gender, family income, and referral source
Age in years, gender, and race/ethnicity (Caucasian, African American, Latino, Asian, or other) were reported by participants at TADS study entry. Race/ethnicity was dichotomized as majority (non-Latino White) or minority because of limited sample sizes. Parents reported annual family income and whether they had been referred from a clinic or were responding to a study advertisement.
Duration of index major depressive episode
An independent evaluator completed a K-SADS-PL interview and estimated the date of onset and duration in weeks of the index episode of MDD at the point of entry into TADS.
CDRS-R
The CDRS-R is a 17-item symptom interview completed by the independent evaluator with reference to the past week, which yields an overall severity score. It has high internal consistency (α = .85) as well as high test–retest (Poznanski & Mokros, 1996) and interrater reliability (intraclass correlation coefficient = .95; TADS Team, 2004).
Reynolds Adolescent Depression Scale (RADS; Reynolds, 1987b)
Adolescents completed the RADS, a 30-item scale pertaining to the past week, to assess self-reported depression severity. The RADS has high internal consistency (α = .92) and test–retest reliability (r = .80; Reynolds, 1987b).
Suicidal Ideation Questionnaire–Junior High Version (SIQ-Jr; Reynolds, 1987a)
The 15-item SIQ-Jr was completed by adolescents to assess severity of suicidal ideation. The SIQ-Jr has high internal consistency (α = .91) and test–retest reliability (r = .89; Reynolds, 1987a).
CGAS
The independent evaluator assigned a rating of general functioning for the past week on the 100-point CGAS. This scale has good reliability and validity (Shaffer et al., 1983).
Comorbidity
In addition to MDD, the K-SADS-PL yielded baseline diagnoses of current dysthymia, any anxiety, disruptive behavior, alcohol or substance use, eating, or tic disorder, and total number of comorbid disorders. The disruptive behavior disorders included conduct disorder, oppositional defiant disorder, and attention-deficit/hyperactivity disorder. The anxiety disorders included general anxiety disorder, separation anxiety disorder, social phobia, posttraumatic stress disorder, panic disorder, and agoraphobia.
Personal Experience Screening Questionnaire (PESQ; Winters, 1991)
Adolescents completed the PESQ, which includes a well standardized 18-item Problem Severity score that measures the extent to which the adolescent is psychologically and behaviorally involved with alcohol or other drugs. Scores range from 18 to 72. Internal consistency reliability (.90–.95) and validity have been established with normal, delinquent, and substance abusing adolescents (Winters, 1991).
TADS Short-Term Depression Treatment Response
In the TADS project, positive short-term treatment response at Week 12 was defined as a rating by an independent evaluator of 1 (very much improved) or 2 (much improved) on the 7-point CGI-I. Non-response was defined as ratings of three (minimally improved) or higher. We compared TADS responders to non-responders using this definition.
To facilitate comparison with other depression treatment studies, we supplemented the above definition of short-term treatment response with a second, more stringent definition used in similar studies, for example, the Treatment of Resistant Depression in Adolescents study (TORDIA; Brent et al., 2008). This second definition of response required both a CGI-I of 1 or 2 and a 50% reduction in CDRS-R raw score. We designated those adolescents who met this definition as symptom count responders.
Course of MDD
MDD recovery, recurrence, and persistence
Recovery from the index episode of MDD was defined as absence of any MDD symptoms for a period of at least 8 weeks. Recurrence of MDD was defined as a new episode following recovery. Chronic or persistent MDD was defined as an index episode from which the adolescent never recovered over the entire TADS–SOFTAD period.
Frequency of Alcohol or Marijuana Use
To determine whether participants with diagnoses of AUD or SUD during SOFTAD were using alcohol or drugs more frequently than other participants, all participants completed 7-point frequency ratings at each SOFTAD assessment point. Each rating indicated frequency of use of alcohol, marijuana, or hard drugs over the past month, with the following intervals: none, 1–2 times, 3–5 times, 6–9 times, 10–19 times, 20–39 times, or over 40 times.
Statistical Analysis
Non-directional hypotheses were tested, and the level of significance was set a .05 for each two-tailed test. Due to the exploratory nature of the study, the alpha was not adjusted for multiple tests.
First, we compared the demographic and clinical characteristics of the TADS participants who were included in the present study (N = 192) to those who were not (N = 247) using general linear models for continuous measures and chi-square tests for binary outcomes. Alternatively, a Wilcoxon Two-Sample Test or Fisher's Exact Test was used when the assumptions of the corresponding parametric test were not met.
As a check on the validity of AUD and SUD diagnoses, we compared participants with AUD to those without AUD on maximum reported frequency of past month alcohol use, using a non-parametric median test. Similarly, we compared SUD to non-SUD participants on highest reported past month frequency of their drug of abuse (cannabis or hard drugs).
The primary outcomes were rates of AUD and SUD for the 192 participants in the present study. Potential predictors of AUD or SUD were grouped in two clusters: (1) short-term treatment response variables and (2) prerandomization baseline variables. Within the first cluster were the two definitions of treatment response: TADS response and symptom count response. Individual bivariate logistic regressions were conducted on each of these separately. In the second cluster, individual bivariate logistic regressions were conducted on each candidate predictor, and measures that were significant at the .10 level were included in a subsequent multivariable logistic regression. We selected this inclusion criterion because it is sometimes possible for an explanatory variable that had a tendency to influence the outcome in bivariate models (p < .10) to become a statistically significant predictor of the outcome (p < .05) when evaluated in the multivariable context. Thus, we selected a liberal .10 significance level as inclusion criterion for the multivariable model to avoid premature elimination of potentially significant predictors (Jaccard, Guilamo-Ramos, Johansson, & Bouris, 2006). Next, multivariable logistic regression analysis was conducted, and a stepwise variable selection approach was applied to derive the most parsimonious baseline variables prediction model. For the stepwise procedure, an entry criterion of .10 and a retention criterion of .05 were specified. The resulting multivariable model only included those variables that were significant at the .05 level after taking into account the relative contribution of the other predictor variables. Each step of multivariable regression analysis was checked for multicollinearity and violation of model assumptions.
Following these analyses, each of the two Week 12 treatment response measures, if individually significant, was added in separate final multivariable models to evaluate the effects of acute treatment response after controlling for other (baseline) predictors in the model.
Secondary exploratory analyses were conducted to determine whether assignment to, or response to, any of the four initial TADS treatments were predictive of subsequent AUD or SUD. We compared rates of subsequent AUD or SUD across the four treatment conditions using chi-square tests. Exploratory logistic regression analyses were then conducted with potential predictors of AUD or SUD that included initial treatment assignment, treatment response, and the interactions of treatment assignment with response. Separate analyses were conducted using each of the two definitions of response.
Finally, logistic regression was employed to examine the association between MDD course (ordered as 0 = recovery with no recurrence, 1 = recovery with one or more recurrences, 2 = persistent depression) and the development of AUD or SUD. Among those who experienced MDD recurrence following recovery, we then described the relation between timing of the recurrence and onset of the AUD or SUD.
Results Preliminary Analyses
Comparing TADS participants who did not participate in SOFTAD and the four SOFTAD participants excluded from the present study because of prior AUD or SUD to the current study participants, we found that participants and non-participants did not differ on percentages randomized to the four TADS treatment conditions, χ2(3, N = 439) = 1.70, p = .64. The percentage of current study participants who had been randomized to each short-term treatment condition was COMB = 25%, FLX = 24%, CBT = 28%, and PBO = 23%.
Table 1 includes comparisons of participants and non-participants on variables related to our hypotheses and on demographic and clinical variables at TADS baseline. There were no differences in percentage of TADS treatment responders (53.6% vs. 51.0%), χ2(1, N = 439) = 0.30, p = .58, or percentages of symptom count responders (47% vs. 44.5%), χ2(1, N = 439) = 0.24, p = .62. The only significant demographic differences were that study participants were somewhat younger than non-participants (M = 14. 3, SD = 1.5 vs. M = 14.8, SD = 1.6), F(1, 437) = 9.22, p = .0025, and included a smaller percentage of minority adolescents (21.4% vs. 30.0%), χ2(1, N = 439) = 4.14, p = .04. The significant baseline clinical differences were that study participants were more likely than non-participants to have entered TADS during their initial episode of MDD (90.5% vs. 82.5%), χ2(1, N = 429) = 5.59, p = .02, and had fewer total comorbid disorders (Mdn = 0 vs. 1; z = –2.51, p = .012). Participants' involvement with alcohol or drugs at baseline was also significantly lower than that of non-participants (PESQ Problem Severity M = 21.2, SD = 6.0 vs. M = 23.0, SD = 7.7), F(1, 422) = 6.81, p = .009.
Rates of Subsequent AUD and SUD
Of the 192 participants, 49 (25.5%) developed an AUD or SUD during the 60 months following short-term depression treatment. As shown in Table 2, 37 (19.3%) developed an AUD, and 34 (17.7%) developed an SUD. These rates are not significantly different from each other (McNemar test p = .70). Twenty-two adolescents (11.5%) developed both disorders. The mean onset age of AUD was 18.0 years (SD = 1.7), and for SUD, the mean onset age was 17.4 years (SD = 1.7). As indicated in Table 2, one third of those with initial SUD-only went on to develop AUD as well, whereas none of those with initial AUD-only proceeded to also develop SUD during the follow-up period. Perhaps related to the slightly older age of onset of AUD compared to SUD in this sample, initial diagnoses of AUD were about equally likely to be made in K-SADS interviews with only the adolescent (20 of 37 or 54.0%) or with the adolescent and a parent (17 of 37 or 45.9%), whereas initial diagnoses of SUD were more likely to be made in K-SADS interviews with the adolescent and a parent (22 of 34 or 64.7%) than in interviews with the adolescent alone (12 of 34 or 35.3%). Those who developed AUD did not differ from those who did not, on their average month of initial SOFTAD assessment, t(190) = 1.35, p = .178. Similarly, those who developed SUD did not differ from those who did not on this measure, t(190) = 1.15, p = .251.
Onset of AUD and/or SUD in 192 Adolescents Over 5 Years Following Treatment for MDD
Among the illicit drugs of abuse, marijuana was the most prevalent drug of abuse, accounting for 26 of the 34 SUD diagnoses. Cocaine, opiates, hallucinogens, other drugs, or polydrug use accounted for the other diagnoses. As a verification of diagnoses, the median peak score for participants with an AUD on past month drinking frequency was 6–9 times versus a median of 1–2 times for those without AUD (z = 4.64, p < .0001). The median peak score for those with an SUD on past month drug use frequency was 10–19 times per month versus a median of no use for those without SUD (z = 5.35, p < .0001).
Treatment Response Analysis
Using logistic regression analysis, we tested whether response to short-term depression treatment reduced the probability of developing either AUD or SUD, using both the TADS response and the symptom count response measures. For AUD, the hypothesis was not confirmed using either definition of response. Among 103 TADS treatment responders, 18 (17.5%) developed AUD; among 89 non-responders, 19 (21.4%) developed AUD, χ2(1, N = 192) = 0.46, odds ratio (OR) = 1.28, 95% CI [0.62, 2.63], p = .498. Among 90 symptom count responders, 17 (18.9%) developed AUD; among 102 non-symptom count responders, 20 (19.6%) developed AUD, χ2(1, N = 192) = 0.02, OR = 1.05, 95% CI [0.51, 2.15], p = .899.
We explored whether randomized treatment assignment, or response to a specific treatment, reduced the probability of developing AUD. Across the four randomized treatment arms, rates of subsequent AUD were 20.8% (COMB), 14.9% (FLX), 20.8% (CBT), and 20.5% (PBO), χ2(3, N = 192) = 0.76, p = .86. Neither this overall comparison nor a post hoc comparison of FLX (which had the lowest rate) to the other three treatments indicated significant differences between treatments in reducing the probability of developing AUD. For the comparison of FLX to other treatments, the percentages developing subsequent AUD were 14.9% and 20.7%, respectively, χ2(1, N = 192) = 0.76, p = .384. An exploratory logistic regression analysis including treatment assignment, treatment response and the interactions of treatment assignment and treatment response as predictors of AUD was not significant, regardless of whether the more global TADS measure of response, or the more restrictive symptom count response, was used in the analysis. For the full model using the TADS response measure, χ2(7, N = 192) = 2.92, p = .892. With the symptom count response measure, χ2(7, N = 192) = 3.065, p = .879.
For SUD, the hypothesis was confirmed: Response to MDD treatment reduced the probability of subsequent SUD. This finding occurred with both measures of response. Twelve of 103 TADS treatment responders (11.6%) developed an SUD versus 22 of 89 non-responders (24.7%), χ2(1, N = 192) = 5.38, OR = 2.49, 95% CI [1.15, 5.38], p = .02. Nine of 90 symptom count responders (10%) versus 25 of 102 non-symptom count responders (24.5%) developed an SUD, χ2(1, N = 192) = 6.52, OR = 2.92, 95% CI [1.28, 6.66], p = .011.
Exploratory analyses showed no significant differences in rates of subsequent SUD across the four TADS treatment conditions, with SUD rates of 14.6% (COMB), 17.0% (FLX), 20.8% (CBT), and 18.2% (PBO), χ2(3, N = 192) = 0.68, p = .88. Neither this overall comparison nor a post hoc comparison of COMB (which had the lowest rate) to the other three treatments indicated significant differences between treatments in reducing the probability of developing SUD. For the comparison of COMB to other treatments, the percentages developing subsequent SUD were 14.6% and 18.8%, respectively, χ2(1, N = 192) = 0.43, p = .514. When the four assigned treatments, treatment response, and the interactions between assigned treatments and response were entered into exploratory logistic regression analyses, the predictive models showed trends toward statistical significance, using either measure of response: With TADS response, χ2(7, N = 192) = 12.11, p = .097; with symptom count response, χ2(7, N = 192) = 12.23, p = .093. However, because neither model attained statistical significance, further analyses were not warranted.
Baseline Predictors Analysis
To evaluate the effects of MDD treatment on AUD and SUD in the context of possible significant baseline predictors, we next tested whether TADS baseline demographic and clinical variables predicted subsequent AUD or SUD. Because of skewed distributions, index episode duration and number of comorbid disorders were natural log transformed. Results are depicted in Table 3.
Individual Logistic Regression Analyses: TADS Baseline Predictors of Subsequent Alcohol or Substance Use Disorder
For subsequent AUD, older age, χ2(1, N = 192) = 8.81, OR = 1.49, 95% CI [1.14, 1.93], p = .003, and higher alcohol or drug involvement, χ2(1, N = 185) = 11.93, OR = 1.11, 95% CI [1.05, 1.18], p < .001, were significant individual predictors. Those who developed AUD averaged 15.0 years of age at baseline (SD = 1.4 years) versus 14.1 years (SD = 1.5 years) for other participants. Adolescents who developed AUD had mean baseline PESQ scores of 24.8 (SD = 8.2), whereas those who did not averaged 20.3 (SD = 4.9).
There was a trend for male adolescents to have lower risk for subsequent AUD than female adolescents. Among 84 male adolescents, 11 (13.1%) developed AUD, whereas 26 of 108 female adolescents (24.1%) did so, χ2(1, N = 192) = 3.56, OR = 0.48, 95% CI [0.22, 1.03], p = .059. There was also a trend for youths with longer episodes of MDD prior to TADS treatment to be more likely to develop later AUD, χ2(1, N = 192) = 3.05, OR = 1.37, 95% CI [0.96, 1.95], p = .081.
When baseline PESQ score, MDD episode duration, age, and gender were entered into a stepwise model, older age, χ2(1, N = 185) = 5.13, OR = 1.37, 95% CI [1.04, 1.81], p = .024, and higher PESQ score, χ2(1, N = 185) = 9.16, OR = 1.10, 95% CI [1.03, 1.16], p = .002, were retained as significant predictors of subsequent AUD. No further multivariable model was tested because MDD treatment response had not proven to be a significant predictor.
For subsequent SUD, significant individual baseline predictors included the total number of comorbid disorders, χ2(1, N = 192) = 5.78, OR = 2.39, 95% CI [1.17, 4.85], p = .016, and the PESQ Problem Severity score, χ2(1, N = 185) = 7.13, OR = 1.08, 95% CI [1.02, 1.14], p = .008. Depressed adolescents who later developed SUD had a mean of 1.1 comorbid disorders (SD = 1.3) compared to a mean of 0.6 comorbid disorders (SD = 0.9) for those who did not. They also had higher PESQ scores at baseline (M = 24.1, SD = 8.4) than other adolescents (M = 20.6, SD = 5.2).
When these two predictors were entered into a stepwise multivariable model, both were retained as significant predictors: PESQ, χ2(1, N = 185) = 7.25, OR = 1.08, 95% CI [1.02, 1.14], p = .007; number of comorbid disorders, χ2(1, N = 185) = 4.17, OR = 2.23, 95% CI [1.03, 4.79], p = .04.
We then tested whether poor treatment response predicted subsequent SUD when the two significant baseline predictors were included in overall models using the two definitions of treatment response. Results indicated that it did. With TADS treatment response in the final model, all three predictors were significant: TADS treatment response, χ2(1, N = 185) = 3.84, OR = 2.30, 95% CI [0.999, 5.28], p = .050; PESQ, χ2(1, N = 185) = 6.48, OR = 1.07, 95% CI [1.02, 1.14], p = .011; number of comorbid disorders, χ2(1, N = 185) = 4.12, OR = 2.23, 95% CI [1.03, 4.86], p = .042. Similarly, with symptom count response in the model, all three predictors remained significant: symptom count response, χ2(1, N = 185) = 4.65, OR = 2.61, 95% CI [1.09, 6.24], p = .031; PESQ, χ2(1, N = 185) = 6.95, OR = 1.07, 95% CI [1.02, 1.14], p = .008; number of comorbid disorders, χ2(1, N = 185) = 4.28, OR = 2.29, 95% CI [1.04, 5.02], p = .038. Characteristics of adolescents who developed AUD, SUD, or neither are described in Table 4.
Characteristics of Adolescents Who Developed AUD, SUD, or Neither, Following Treatment for MDD
MDD Course Analysis
The course of MDD for study participants through the end of SOFTAD was as follows: 98 (51.0%) recovered from their index episode with no recurrence, 87 (45.3%) recovered but had at least one recurrence, and 7 (3.7%) experienced chronic MDD. AUD was diagnosed in 10 of the 98 recovery cases (10.2%), in 25 of the 87 recovery and recurrence cases (28.7%), and in 2 of the 7 persistent depression cases (28.6%). Comparing the 98 recovery cases to the 94 cases with either chronic or recurrent MDD, a logistic regression indicated that depression recovery was negatively associated with onset of AUD, χ2(1, N = 192) = 9.8, OR = 0.28, 95% CI [0.13, 0.62], p = .002.
SUD was diagnosed in 13 of the 98 recovery cases (13.3%), in 19 of the 87 recovery and recurrence cases (21.8%), and in 2 of the 7 chronic depression cases (28.6%). Logistic regression indicated a trend for the depression recovery group to have fewer cases of SUD onset, χ2(1, N = 192) = 2.77, OR = 0.53, 95% CI [0.24, 1.14], p = .103.
Lastly, we explored the timing of MDD recurrence in relation to AUD or SUD onset. Among the 87 participants with recurrent MDD, 62 (71.3%) did not develop AUD, one (1.1%) had MDD recurrence before AUD onset, and 24 (27.6%) had MDD recurrence after AUD onset. In this latter group, the onset of first MDD recurrence was, on average, 22.7 months (SD = 11.8) after the AUD onset. A similar pattern was observed for MDD recurrence and SUD: 68 participants with recurrent MDD (78.2%) did not develop SUD, two (2.3%) had MDD recurrence before SUD onset, and 17 (19.5%) had MDD recurrence after SUD onset. Onset of the first recurrence was, on average, 19 months (SD = 9.9) after SUD onset.
DiscussionWe followed the largest sample to date of adolescents who had been treated for MDD, and we restricted the focus of the present study to those with no preexisting AUD or SUD to determine whether effective MDD treatment reduced the likelihood of developing either AUD or SUD. Five years after the end of short-term depression treatment, a quarter of the sample (25.5%) had developed either AUD or SUD. Positive response to short-term depression treatment was not related to later onset of AUD but lowered the likelihood of future SUD, even when baseline predictors of SUD were taken into account. Significant baseline predictors of AUD were older age and greater involvement with alcohol or drugs at entry into treatment. Significant baseline predictors of SUD were comorbid disorders and greater involvement with alcohol or drugs at entry into treatment.
The prevalence of AOSUDs in this sample of adolescents and young adults can be put in perspective by comparison with community or epidemiological studies. At the end of the follow-up period, the mean age of our participants was 20.1 years (range = 17–23). The most recent National Survey on Drug Use and Health (NSDUH; SAMHSA, 2010) indicated that the age group of 18–25 had the highest rate of past year diagnoses of AUD or SUD (20.8%) among three broad age groups surveyed (12- to 17-year-olds had a rate of 7.6%; those 26 years of age or older had a rate of 7.0%). Two other studies of younger adolescents yielded lifetime diagnoses for AOSUDs that were lower than those in our sample (12.2% by 16 years of age and 10.8% by 18 years of age; Costello et al., 2003; and Lewinsohn et al., 1993, respectively). The National Comorbidity Study replication (NCS-2; Kessler et al., 2005) did not include participants under 18 years of age but reported an AOSUD rate of 16.7% for those 18–29 years of age. Based on comparison with these studies, the rate of AOSUDs in our sample of treated, formerly depressed adolescents (25.5%) is most similar to, but exceeds that of, the NSDUH for 18- to 25-year-olds. Given methodological differences, and lacking a direct comparison with matched non-depressed adolescents, our study cannot conclusively state that the rate of AOSUDs in formerly depressed adolescents is elevated, but the rate is high enough to warrant concern and further study. In addition, as discussed below, the overall AOSUDs rate may have been even higher if all TADS adolescents had participated in the follow-up study. Finally, we did not follow a group of untreated depressed adolescents to determine whether the overall rate of subsequent AOSUDs would have been even more elevated in the absence of treatment.
Also of note is the relative frequency of AUD (19.3%) and SUD (17.7%) in our sample. Most epidemiological studies indicate that AUD occurs at a higher frequency than SUD, whereas in our study the two rates were not significantly different. A New York study (Cohen et al., 1993) found alcohol abuse far more prevalent in the 17- to 20-year-old age group than marijuana abuse (14.6% vs. 2.9%). AUD was also more prevalent than SUD among 18- to 29-year-olds in the NCS-2 (Kessler et al., 2005) and about 2.5 times more prevalent in the most recent NSDUH, affecting 7.3% of the U.S. population ages 12 through adulthood compared to 2.8% for SUD. An exception to this pattern was an Oregon study (Lewinsohn et al., 1993) that found SUD somewhat more prevalent at 18 years of age than AUD (8.2% vs. 6.2%), suggesting that although AUD is typically more prevalent than SUD, relative rates can be affected by geographic or temporal factors.
We found that SUD was predicted by comorbid psychopathology at baseline and by failure to respond to short-term depression treatment, whereas AUD was predicted by older age, a normal developmental factor, and not by depression treatment response. Considering these findings in the context of the relatively high prevalence rate of SUD in our sample, there may be a stronger link among depressed adolescents between adolescent psychopathology and subsequent SUD than there is for AUD. Reduction in overall psychopathology through successful depression treatment may have had more impact in preventing SUD than in preventing AUD because of such a link. By contrast, AUD tends to become elevated in the age range we studied, as alcohol use becomes more normative and part of social interactions. These possibilities are, of course, speculative, but they are consistent with an earlier cross-sectional study of college students in which MDD was associated with both AUD and SUD, but only SUD was also associated with comorbid diagnoses (Deykin et al., 1987).
Exploratory analyses indicated that no specific TADS MDD treatment proved more effective than others in reducing risk of subsequent AUD or SUD. This finding should not be interpreted to indicate that failure to actively treat adolescent MDD would be as effective as the TADS treatments in reducing risk for subsequent AUD or SUD. Three of the four TADS conditions involved an active treatment, and the acute phase PBO condition included regular clinical contact, support, and symptom reviews during the first 12 weeks, generally followed by open treatment after the blind was broken (Kennard et al., 2009). Moreover, both the present study and an earlier report on this sample indicate that attaining a full response to acute depression treatment is important. In the previous study (Curry et al., 2011), a positive short-term treatment response predicted greater likelihood of full recovery from MDD within 2 years, whereas the present study indicated that full response to treatment lowered the risk of subsequent SUD.
Our findings indicated that positive response to depression treatment, rather than engagement in a specific treatment, reduced risk of subsequent SUD. This is consistent with studies indicating that depressive symptoms are a risk factor for later substance abuse in older adolescents (e.g., Lewinsohn, Gotlib, & Seeley, 1995). The mechanisms through which effective depression treatment reduces risk for later SUD require further research and may vary by treatment. It is possible that problem-solving and coping skills learned in the CBT and COMB conditions, which parallel effective components of substance abuse treatment (Waldron & Turner, 2008), contributed to this positive outcome. Similarly, improved mood regulation due to medication effects, or shared elements common to all four interventions (support, psychoeducation about depression), may have been effective mechanisms. Alternatively, because adolescent depression has a negative impact on peer, family, and academic functioning (Jaycox et al., 2009), it is possible that TADS treatment responders' improved functioning, which was accounted for by reduced depression (Vitiello et al., 2006), reduced their risk for subsequent involvement with substances.
We found a trend (p = .059) for female participants to have higher rates of AUD than male participants. This stands in contrast to the general finding that adult men have higher rates of AUD than adult women. However, the gender difference in prevalence of AUD begins to emerge only around 18 years of age and is less significant among adults who have both depression and AUD (Schulte, Ramo, & Brown, 2009). In our sample of formerly depressed adolescents, female gender was not a protective factor against development of AUD.
A more negative course of MDD after acute treatment was significantly associated with AUD onset in the present sample, with a comparable trend result for SUD. Recurrent or chronic MDD was linked to higher probability of an AUD. This finding is consistent with previously noted associations between more prolonged depression and alcohol or substance abuse (King et al., 1996) in adolescent girls. When participants in the present study developed both recurrent depression and AUD, the AUD most often occurred prior to the recurrent episode of MDD. The present findings suggest that AUD raised the risk of MDD recurrence, rather than recurrence increasing the probability of AUD.
Clinical Implications
The importance of attaining a full response to MDD treatment, regardless of type, is reinforced by the present findings. The significance of attaining a full response to short-term treatment in reducing risk for SUD was evident even when considering other significant risk factors for SUD. Thus, augmenting or changing partially effective MDD treatments after a relatively brief acute intervention period is recommended for achieving the secondary benefit of reduced SUD risk. For depressed adolescent non-responders to selective serotonin reuptake inhibitors, augmenting medication treatment with CBT significantly improved outcome (Brent et al., 2008). No parallel study has been completed to investigate the augmenting effect of medication among non-responders to CBT, but clinical guidelines advocate augmenting or changing ineffective psychotherapy after a reasonable period of time (Hughes et al., 2007).
Depressed adolescents who later develop AUD or SUD are more likely than those who do not to already be using alcohol or drugs at the time they enter depression treatment. Indeed, a single score indicative of such involvement predicted both AUD and SUD. When combined with older age, alcohol or drug involvement at entry into depression treatment predicted AUD, and when combined with comorbid disorders, it predicted SUD. Thus, it is important to assess all levels of alcohol and drug use before starting treatment for adolescent depression to monitor depressed adolescents who are using alcohol or drugs and to intervene quickly if AUD or SUD develops.
After recovery from adolescent MDD, AUD significantly increased the likelihood of depression recurrence, with a similar trend for SUD. Thus, our findings are more consistent with a “drinking consequences” model than with a “self-medication” model of the relation between negative mood and drinking (Hussong, Gould, & Hersh, 2008), at least among adolescents with a history of MDD. Adolescents who have experienced successful treatment for MDD and their parents should be advised of the risk for recurrence that is associated with significant alcohol misuse. Formerly depressed adolescents who then develop AUD or SUD should be monitored for a return of depressive symptoms and should be offered interventions to reduce risk of a recurrent depressive episode.
Limitations
Although this is the largest sample of treated depressed adolescents with long-term follow-up data, power to detect a significant difference when testing the main hypothesis was limited. For example, with 103 responders and 89 non-responders on the TADS acute treatment response measure, power to detect a significant difference between the rates of subsequent SUD in these two groups (11.6% vs. 24.7%, respectively) was only .64. Moreover, the number of participants who developed AUD or SUD was relatively small. Therefore, conclusions based on rates of AUD or SUD must be viewed with caution, pending replication.
Another significant study limitation is that the SOFTAD sample consisted of slightly under half of the initial TADS sample. Previous follow-up studies of treated depressed adolescents have retained higher rates of participants than did SOFTAD, ranging from 97% (Birmaher et al., 2000) to approximately 60% of originally randomized and treated adolescents (Clarke, Rohde, Lewinsohn, Hops, & Seeley, 1999). However, these studies were conducted in one or two sites over 2 years, compared to the present multisite, 5-year project, in which both retention of later TADS completers and recontacting of early TADS completers were required. As has been reported by others (Badawi, Eaton, Myllyluoma, Weimer, & Gallo, 1999; Cotter, Burke, Loeber, & Navratil, 2002), our retention was more challenging with older adolescents and with minority participants. On most indices, the SOFTAD sample was representative of the full TADS sample, but our sample was somewhat younger, had fewer comorbid disorders, and had less involvement with alcohol or drugs at TADS baseline than non-participants who had been in the TADS sample. All three of these factors were associated in our follow-up sample with lower likelihood of developing AUD or SUD. Therefore, it is very possible that the rates of these later disorders might have been higher if the entire TADS sample had participated in the extended follow-up.
Also, because the SOFTAD sample was derived entirely from the TADS sample, it was limited to adolescents who passed TADS exclusion criteria. No adolescents with bipolar disorder, severe (violent or assaultive) conduct disorder, pervasive developmental disorder, or thought disorder were included. The first two exclusions, in particular, may also have led to lower rates of subsequent AUD or SUD than might be found in a depressed adolescent outpatient sample not similarly restricted.
Our study is also limited by the lack of a non-depressed comparison group. Without such a direct comparison, we cannot be certain that the rates of AUD or SUD among formerly depressed adolescents exceed those of similar but non-depressed adolescents. Finally, for obvious ethical reasons, we did not include a group of untreated adolescents with MDD. Therefore, we do not know the rates of subsequent AUD or SUD among depressed young people who are untreated.
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Submitted: January 20, 2011 Revised: November 1, 2011 Accepted: November 16, 2011
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Source: Journal of Consulting and Clinical Psychology. Vol. 80. (2), Apr, 2012 pp. 299-312)
Accession Number: 2012-00540-001
Digital Object Identifier: 10.1037/a0026929
Record: 37- Title:
- Pathways from childhood abuse and neglect to HIV-risk sexual behavior in middle adulthood.
- Authors:
- Wilson, Helen W.. Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago, IL, US, helen.wilson@rosalindfranklin.edu
Widom, Cathy Spatz. John Jay College of Criminal Justice, City University of New York, NY, US - Address:
- Wilson, Helen W., Department of Psychology, Rosalind Franklin University of Medicine and Science, 3333 Green Bay Road, North Chicago, IL, US, 60064, helen.wilson@rosalindfranklin.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 79(2), Apr, 2011. pp. 236-246.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- HIV risk, child abuse and neglect, sexual behavior, psychosocial factors, sexual risk taking
- Abstract:
- Objective: This study examines the relationship between childhood abuse and neglect and sexual risk behavior in middle adulthood and whether psychosocial factors (risky romantic relationships, affective symptoms, drug and alcohol use, and delinquent and criminal behavior) mediate this relationship. Method: Children with documented cases of physical abuse, sexual abuse, and neglect (ages 0–11) processed during 1967–1971 were matched with nonmaltreated children and followed into middle adulthood (approximate age 41). Mediators were assessed in young adulthood (approximate age 29) through in-person interviews between 1989 and 1995 and official arrest records through 1994 (N = 1,196). Past year HIV-risk sexual behavior was assessed via self-reports during 2003–2004 (N = 800). Logistic regression was used to examine differences in sexual risk behavior between the abuse and neglect and control groups, and latent variable structural equation modeling was used to test mediator models. Results: Child abuse and neglect was associated with increased likelihood of risky sexual behavior in middle adulthood, odds ratio = 2.84, 95% CI [1.74, 4.64], p ≤ .001, and this relationship was mediated by risky romantic relationships in young adulthood. Conclusions: Results of this study draw attention to the potential long-term consequences of child abuse and neglect for physical health, in particular sexual risk, and point to romantic relationships as an important focus of intervention and prevention efforts. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Child Abuse; *Child Neglect; *HIV; *Psychosocial Factors; *Sexual Risk Taking
- Medical Subject Headings (MeSH):
- Adult; Adult Survivors of Child Abuse; Child; Child, Preschool; Female; HIV Infections; HIV Seropositivity; Humans; Infant; Interpersonal Relations; Male; Middle Aged; Odds Ratio; Regression Analysis; Risk Factors; Risk-Taking; Sexual Behavior; Substance-Related Disorders
- PsycINFO Classification:
- Behavior Disorders & Antisocial Behavior (3230)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Childhood (birth-12 yrs)
Neonatal (birth-1 mo)
Infancy (2-23 mo)
Preschool Age (2-5 yrs)
School Age (6-12 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs) - Tests & Measures:
- National Institute of Mental Health Diagnostic Interview Schedule—Revised
HIV-Risk Sexual Behavior measure - Grant Sponsorship:
- Sponsor: Eunice Kennedy Shriver National Institute of Child Health and Human Development
Grant Number: HD40774
Recipients: No recipient indicated
Sponsor: National Institute of Mental Health
Grant Number: MH49467; MH58386
Recipients: No recipient indicated
Sponsor: National Institute of Justice
Grant Number: 86-IJ-CX-0033; 89-IJ-CX-0007; 93-IJ-CX-0031
Recipients: No recipient indicated
Sponsor: National Institute on Drug Abuse
Grant Number: DA17842; DA10060
Recipients: No recipient indicated
Sponsor: National Institute on Alcohol Abuse and Alcoholism
Grant Number: AA09238; AA11108
Recipients: No recipient indicated
Sponsor: Doris Duke Charitable Foundation
Recipients: No recipient indicated - Methodology:
- Empirical Study; Longitudinal Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Feb 28, 2011; Accepted: Dec 20, 2010; Revised: Nov 17, 2010; First Submitted: Jun 22, 2010
- Release Date:
- 20110228
- Correction Date:
- 20110328
- Copyright:
- American Psychological Association. 2011
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0022915
- PMID:
- 21355638
- Accession Number:
- 2011-04114-001
- Number of Citations in Source:
- 51
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2011-04114-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2011-04114-001&site=ehost-live">Pathways from childhood abuse and neglect to HIV-risk sexual behavior in middle adulthood.</A>
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Pathways From Childhood Abuse and Neglect to HIV-Risk Sexual Behavior in Middle Adulthood
By: Helen W. Wilson
Department of Psychology, Rosalind Franklin University of Medicine and Science;
Cathy Spatz Widom
John Jay College of Criminal Justice, City University of New York
Acknowledgement: This research was supported in part by National Institute of Child Health and Human Development Grant HD40774; National Institute of Mental Health Grants MH49467 and MH58386; National Institute of Justice Grants 86-IJ-CX-0033, 89-IJ-CX-0007, and 93-IJ-CX-0031; National Institute on Drug Abuse Grants DA17842 and DA10060; National Institute on Alcohol Abuse and Alcoholism Grants AA09238 and AA11108; and by the Doris Duke Charitable Foundation. Points of view are our own and do not necessarily represent the position of the U.S. government. We thank Sally Czaja for her consultation regarding statistical analyses.
Numerous studies have linked childhood maltreatment to risky sexual behavior later in life (e.g., Bensley, Van Eenwyk, & Simmons, 2000; Berenson, Wiemann, & McCombs, 2001; Cunningham, Stiffman, Dore, & Earls, 1994; Dube, Felitti, Dong, Giles, & Anda, 2003; Koenig & Clark, 2004; Mullings, Marquart, & Brewer, 2000; National Institute of Mental Health Multisite HIV Prevention Trial Group, 2001; Noll, Trickett, & Putnam, 2003; Paolucci, Genuis, & Violato, 2001; Purcell, Malow, Dolezal, & Carballo-Diéguez, 2004; Rodgers et al., 2004; Stiffman, Dore, Cunningham, & Earls, 1995). However, the majority of studies have focused only on sexual abuse (Senn, Carey, & Vanable, 2008), and most have relied on retrospective reports of childhood maltreatment. As an exception, findings from a prospective cohort design study revealed that individuals with documented cases of childhood physical abuse, sexual abuse, and neglect, compared to matched controls, were at increased risk for prostitution (Widom & Kuhns, 1996; Wilson & Widom, 2008a) and early sexual initiation (Wilson & Widom, 2008a) assessed in young adulthood. This study also found that victims of child abuse and neglect were more likely than nonmaltreated controls to be HIV positive and to report having had other sexually transmitted diseases in middle adulthood (Wilson & Widom, 2008a, 2009b). The current study expands upon this work to examine links from childhood abuse and neglect to risky sexual behavior in the same sample followed up in middle adulthood, at approximate age 41.
Recent research implicates childhood maltreatment as a risk factor for long-term physical health problems and health risk behaviors (Dube et al., 2003; Rodgers et al., 2004; Walker et al., 1999). Childhood abuse and neglect can result in a cascade of negative effects across multiple domains of physiological, social, psychological, and behavioral development, which may explain increased propensity for risky sexual behavior in adulthood. The self-trauma model (Briere, 1996) suggests that childhood maltreatment can lead to problematic outcomes in adulthood through multiple developmental pathways involving attachment problems; symptoms of posttraumatic stress disorder (PTSD); maladaptive coping; and negatively distorted appraisals of oneself, others, and the future. According to this model, disruption of basic developmental processes can result in chaotic and conflictual relationships, use of poor coping strategies such as substance use and aggression, affective dysregulation, and psychological distress. For example, disruption of the early attachment relationship appears to undermine the development of intimate relationships and emotional regulation. Childhood maltreatment also appears to affect the development of neurological and physiological processes related to stress response, affect regulation, social and emotional development, and cognition (De Bellis, 2001; Glaser, 2000). In an integrative model, Repetti, Taylor, and Seeman (2002) suggest that maladaptive family environments, characterized by anger and aggression, low warmth and support, and neglect, increase risk for health-compromising behaviors through deficits in children's emotional control and expression, social competence, and physiological regulation. Thus, deficits in multiple domains of psychosocial functioning may mediate the relationship between childhood maltreatment and risky sexual behavior. However, very little research has directly examined social, emotional, and behavioral mechanisms that may explain the relationship between childhood maltreatment and risky sexual behavior (Senn et al., 2008).
The present study extends and expands on earlier work (Widom & Kuhns, 1996; Wilson & Widom, 2008a, 2009b, 2010b) by examining data collected during middle adulthood with a sample of individuals with documented histories of child abuse and neglect and of matched controls. We had two primary hypotheses. First, we predicted that victims of childhood abuse and neglect would be more likely than controls to report HIV-risk sexual behavior in middle adulthood. We expected this relationship would apply for three forms of child maltreatment: sexual abuse, physical abuse, and neglect. Second, we predicted that a set of psychosocial mediators (risky relationships, affective symptoms, drug and alcohol use, and delinquent and criminal behavior) would explain the relationship between childhood maltreatment and risky sexual behavior. We compared these potential mediating pathways to determine which specific risk factors were most likely to explain increased sexual risk in victims of childhood abuse and neglect.
Method Design and Participants
Data were collected as part of a large prospective cohort design study in which abused and/or neglected children were matched with nonabused, nonneglected children and followed into adulthood. Because of the matching procedure, participants are assumed to differ only in the risk factor (i.e., whether they have experienced childhood sexual or physical abuse or neglect). Because it is not possible to assign participants randomly to groups, the assumption of equivalency for the groups is an approximation. The control group may also differ from the abused and neglected individuals on other variables nested with abuse or neglect. For complete details of the study design and participant selection criteria, see Widom (1989a).
The original sample of abused and neglected children (N = 908) was made up of substantiated cases of childhood physical and sexual abuse and neglect processed from 1967 to 1971 in the county juvenile (family) or adult criminal courts of a midwestern metropolitan area. Cases of abuse and neglect were restricted to children 11 years of age or younger at the time of the incident and, therefore, represent childhood maltreatment. A control group of children without documented histories of childhood abuse and/or neglect (N = 667) was matched with the abuse and/or neglect group on age, sex, race or ethnicity, and approximate family social class during the time that the abuse and neglect records were processed. The abuse and neglect and control groups were identified approximately twenty years after cases of abuse and neglect occurred.
The control group represents a critical component of the design of the study. Children who were under school age at the time of the abuse and/or neglect were matched with children of the same sex, race, date of birth (±1 week), and hospital of birth through the use of county birth record information. For children of school age, records of more than 100 elementary schools for the same time period were used to find matches with children of the same sex, race, date of birth (±6 months), class in elementary school during the years 1967 to 1971, and home address, preferably within a five-block radius of the abused/neglected child. Overall, matches were found for 74% of the abused and neglected children. Nonmatches occurred for a number of reasons. For birth records, nonmatches occurred in situations when the abused and neglected child was born outside the county or state or when date of birth information was missing. For school records, nonmatches occurred because of lack of adequate identifying information for the abused and neglected children or because the elementary school had closed over the last 20 years and class registers were unavailable. Reanalyses of findings on criminal behavior were conducted only with matched pairs (i.e., excluding abused and neglected participants without matches), and the results did not change with the smaller sample size (Widom, 1989b). Court records were searched for individuals identified for the control group, and those found to have cases of abuse or neglect were dropped (N = 11).
The initial phase of the larger longitudinal study compared the abused and/or neglected children to the matched comparison group (total N = 1,575) on juvenile and adult criminal arrest records (Widom, 1989a). A second phase involved tracking, locating, and interviewing the abused and/or neglected and comparison groups during 1989–1995, approximately twenty years after the incidents of abuse and neglect (N = 1,196). This interview consisted of a series of structured and semistructured questionnaires and rating scales, including the National Institute of Mental Health Diagnostic Interview Schedule—Revised (DIS–III–R; Robins, Helzer, Cottler, & Goldring, 1989), a standardized psychiatric assessment that yields Diagnostic and Statistical Manual of Mental Disorders (3rd ed., rev.; DSM–III–R; American Psychiatric Association, 1987) diagnoses. Subsequent follow-up interviews were conducted in 2000–2002 and in 2003–2004. The research presented in this paper uses self-reports and criminal records gathered in the 1989–1995 interviews and information on risky sexual behavior gathered as part of a medical status examination in 2003–2004.
Although there was attrition associated with death, refusals, and our inability to locate individuals over the various waves of the study, the composition of the sample at the four time points has remained about the same. The abuse and neglect group represented 56–58% at each time period; White, non-Hispanics were 62–66% and men made up 48–51% of the samples. There were no significant differences across the samples on these variables or in mean age across the four phases of the study.
Interview, 1989–1995
Of the original sample, 1,307 participants (83%) were located and 1,196 (76%) participated in the first interview. Of those not interviewed, 43 were deceased, eight were unable to be interviewed, 268 were not found, and 60 refused to participate. At this wave of the study, the sample was an average of 29.2 years old (range = 19.0–40.7 years, SD = 3.8) and included 582 women (49%). Based on self-reports of race or ethnicity, 61% of participants were White, non-Hispanic, 33% were Black, 4% were Hispanic, 1.5% were American Indian, and less than 1% were Pacific Islander or other. The median occupational level (Hollingshead, 1975) for the group was semiskilled workers, and only 13% held professional jobs. On average, participants had completed 11.5 years of education (SD = 2.14). Thus, the sample was skewed toward the lower end of the socioeconomic spectrum. Of the 1,196 participants, 520 were in the control group and 676 were in the abuse and/or neglect group (543 cases of neglect, 110 cases of physical abuse, and 96 cases of sexual abuse). These numbers add up to more than 676 because some individuals experienced more than one type of abuse or neglect.
Interview, 2003–2004
A total of 808 individuals completed the third interview, and 800 participants provided information about risky sexual behavior. This sample included 454 cases of abuse and neglect (367 neglect, 78 physical abuse, and 60 sexual abuse) and 346 matched controls. They were on average 41.2 years of age (range 32.0–49.0 years), and 52.9% were women. Based on self-reports of race/ethnic background, 59.0% were White, non-Hispanic, 34.4% were Black, non-Hispanic, 4.0% were Hispanic, and 2.6% were of other racial/ethnic backgrounds. Women from minority backgrounds composed 22% of the sample.
Procedures
Participants completed the interview and comprehensive medical examination in their homes or another place appropriate for the interview, as they preferred. The interviewers were blind to the purpose of the study and to the inclusion of an abused and/or neglected group. Participants were also blind to the purpose of the study and were told that they had been selected to participate as part of a large group of individuals who grew up in the late 1960s and early 1970s. Institutional review board approval was obtained for the procedures involved in this study, and participants who participated gave written, informed consent. For individuals with limited reading ability, the consent form was presented and explained verbally.
Measures
Child abuse and neglect
Childhood physical and sexual abuse and neglect were assessed through review of official records processed during the years 1967 to 1971. Physical abuse cases included injuries such as bruises, welts, burns, abrasions, lacerations, wounds, cuts, bone and skull fractures, and other evidence of physical injury. Sexual abuse cases had charges ranging from relatively nonspecific charges of “assault and battery with intent to gratify sexual desires” to more specific charges, such as “fondling or touching in an obscene manner,” sodomy, incest, or rape. Neglect cases reflected a judgment that the parents' deficiencies in child care were beyond those found acceptable by community and professional standards at the time and represented extreme failure to provide adequate food, clothing, shelter, and medical attention to children.
HIV-risk sexual behavior
As part of a medical history interview in 2003–2004, participants reported whether they had in the past year (a) been treated for a sexually transmitted disease; (b) given or received money or drugs in exchange for sex; (c) had anal sex without a condom; or (d) used intravenous drugs. Participants who endorsed any of the four items were then asked, if comfortable, to identify which situations applied to them. Participants also reported how many sexual partners they had in the past year and whether they used a condom at the last sexual intercourse. A composite score was created to reflect any risky sexual behavior in the past year, and separate items indicated (a) sexually transmitted disease (STD); (b) trading sex; (c) unprotected anal sex; and (d) multiple partners and inconsistent condom use. This study focused only on HIV-risk sexual behavior, because relationships between childhood maltreatment and drug use in this sample are not straightforward (Widom, Marmorstein, & White, 2006).
Risky relationships
Participants were asked a series of questions about intimate relationship functioning in young adulthood as part of the Antisocial Personality Disorder (APD) module of the DIS–III–R. These items asked about lifetime and current involvement in intimate relationships, including whether the participant had ever walked out on a partner for several weeks or longer, been sexually faithful for at least a year, or had sexual relations outside of marriage with at least three people (Colman & Widom, 2004). Additional interview questions asked about intimate relationship history, including marriage and cohabitation. Separate dichotomous variables (1 = yes; 0 = no) were created to reflect (a) walking out on a partner; (b) never sexually faithful; (c) sexual relations outside of marriage; (d) temporary separation from a partner; and (e) multiple marriages.
Affective symptoms
Lifetime symptoms of depression, dysthymia, and PTSD were assessed in young adulthood with the DIS–III–R and therefore correspond to DSM–III criteria for these disorders. Continuous variables reflecting the number of symptoms reported are used in analyses.
Drug and alcohol use
Drug and alcohol use were assessed through self-reports on the DIS–III–R substance use module completed in young adulthood. Three variables were created to reflect (a) number of problems associated with alcohol use; (b) number of problems associated with drug use; and (c) number of illicit drugs used more than five times.
Delinquent and criminal behavior
Delinquency and crime were measured with two variables self-reported in young adulthood and collection of arrest records through 1994: (a) number of officially documented arrests for crimes other than prostitution; (b) self-report on the DIS–III–R APD module of having been arrested (0 = never; 1 = yes); and (c) number of delinquent and criminal behaviors (e.g., property damage, theft, sexual and physical assault, carrying and/or using weapons) reported on a measure adapted from Wolfgang and Weiner (1989).
Control variables
Age in middle adulthood, gender, and race or ethnicity were examined as potential control variables, given differences in rates of HIV risk behavior reported in the literature (Centers for Disease Control and Prevention, 2005; Leigh, Temple, & Trocki, 1993). Gender was coded 1 for men and 0 for women. Race or ethnicity was coded as non-Hispanic Black (1), non-Hispanic White (2), or Hispanic (3). Other racial or ethnic groups were not included in this variable because their proportion of the sample was too small for meaningful comparison (2.3%).
Analyses
Differences between the abuse and/or neglect and control groups in overall HIV risk and each indicator of HIV risk were assessed with logistic regression. Odds ratios (OR) were generated by exponentiation of the regression coefficients. Each regression included all participants with complete data on the variables included in that regression model (i.e., pairwise deletion). Therefore, the sample size differed somewhat depending on the outcome (see Table 1), due to a small number of missing responses (e.g., participant refused to answer a particular question).
Variable Descriptive Statistics and Factor Loadings in Confirmatory Factor Analysis
Latent variable structural equation modeling (SEM) with Mplus Version 5.1 was used to examine mediator models. SEM proceeded in three stages. First, we conducted confirmatory factor analysis to assess the measurement model describing relationships between the observed indicators and latent constructs. Table 1 lists the observed variables that loaded onto each latent factor. Residual correlations among the observed indicators for each latent factor were included in the model. HIV risk behavior was indicated by the single binary composite score, due to the low prevalence of any individual risk behavior, and childhood abuse and neglect was also indicated by a single binary variable. All bivariate correlations between the latent variables, HIV risk behavior, and childhood abuse and neglect were included in the model. Thus, in addition to assessing the fit of the measurement model and significance of factor loadings, confirmatory factor analysis provided a test of the criteria for mediation that (a) the predictor is related to the outcome; (b) the predictor is related to the mediator; and (c) the mediator is related to the outcome (Kenny, Kashy, & Bolger, 1998). Second, we examined separate path models with each mediator, which evaluated the two additional criteria for mediation by each latent factor: (d) the relationship between the mediator and the outcome remains significant when controlling for the predictor and (e) the direct relationship between the predictor and the outcome is significantly reduced when the mediator is included. Third, we examined a multiple-mediator model that included all mediators supported by analysis of the separate path models considering traditional criteria (Kenny et al., 1998) and current recommendations of MacKinnon (2008), as well as the moderated mediation approach recommended by Kraemer, Kiernan, Essex, and Kupfer (2008). Although the Kenny et al. approach to mediation has been criticized as overly conservative, and newer approaches suggest that Criteria 1 and 5 are not essential (MacKinnon, 2008), we considered all criteria in evaluating the potential mediators.
For measurement and structural models, we evaluated multiple indices of overall model fit. A chi-square statistic (χ2) reflects the difference between the observed model relationships and estimated relationships based on the specified model. A low chi-square and nonsignificance (p < .05) are desirable, and a chi-square to degrees of freedom (df) ratio of less than 5 is considered adequate (Bollen, 1989). A comparative fit index (CFI) and Tucker–Lewis index (TLI) of .90 or higher indicate good fit. Root-mean-square error of approximation (RMSEA) of less than .05 is considered a close fit, and weighted root-mean-square residual (WRMR) of less than 1 indicates a good fit. Current recommendations support consideration of both the chi-square test and other indices of model fit (Barrett, 2007); chi-square and WRMR can be overly sensitive to discrepancies between observed and expected relationships with a large sample.
Individual factor loadings (measurement models) and path estimates (structural models) were standardized linear regression coefficients for continuous factor indicators or dependent variables, including latent constructs, and standardized probit regression coefficients for binary (0–1) factor indicators or dependent variables, including HIV risk behavior. Probit coefficients represent change in the cumulative normal probability of the dependent variable associated with a one-unit increase in the predictor. Thus, the magnitudes of coefficients corresponding to continuous and binary outcomes (or factor indicators) are not directly comparable. Statistical significance was assessed with z scores, and R2 provided a measure of effect size, indicating the amount of variance explained in the HIV risk behavior by each model. Strength of mediational relationships was evaluated with tests of indirect effects (MacKinnon, Lockwood, Hoffman, West, & Sheets, 2002) and bias-corrected bootstrapped confidence intervals (MacKinnon, Lockwood, & Williams, 2004).
Full information maximum likelihood estimation was used to handle missing data. This method uses all data available for each case and thus avoids biases and loss of power associated with traditional approaches to missing data (Allison, 2003; Schlomer, Bauman, & Card, 2010). Full information maximum likelihood calculates weighted least square parameter estimates using a diagonal weight matrix with standard errors and mean- and variance adjusted chi-square test statistics that use a full weight matrix, and this estimator is robust to deviations from normality. Analysis of missing data using Mplus revealed 33 specific patterns. The most common pattern reflected participants who had complete data on the young adulthood variables but who were lost to attrition at middle adulthood or did not respond to questions about sexual risk (17%); an additional 217 (18%) were missing sexual risk data as well as at least one young adulthood variable. The next most common pattern (11%) reflected individuals who were missing data on the variables of “temporary separation from a partner” and “never sexually faithful” because they had never had a marital, live-in, or exclusive partner (these individuals were coded 0 for “walking out on a partner,” “multiple marriages,” and “sexual relations outside of marriage”). Other patterns were associated with less than 10% of the sample. SEM analyses including only cases with available data on sexual risk in middle adulthood (N = 778) yielded findings consistent with those reported below.
Results Prevalence of Risky Sexual Behavior in the Sample
Overall, 12.7% of the sample reported at least one type of risky sexual behavior in middle adulthood. In particular, 2.1% reported treatment for an STD, 1.6% reported trading sex, 5.5% reported unprotected anal sex, and 7.2% reported multiple partners with no condom use at last sexual intercourse. Rates of risky sexual behavior were not associated with age, OR = 0.98, 95% CI [0.92, 1.04], p > .10, and did not differ significantly for women and men, OR = 1.09, 95% CI [0.71, 1.66], p > .10, or for different racial or ethnic groups, F(2, 754) = 0.65, p > .10. Therefore, these variables were not controlled in subsequent analyses.
Relationships Between Childhood Abuse and Neglect and Risky Sexual Behavior
As shown in Table 2, child abuse and neglect overall was significantly associated with increased likelihood of any risky sexual behavior, OR = 2.84, 95% CI [1.74, 4.64], p ≤ .001, in the past year and specifically for treatment for an STD, OR = 3.65, 95% CI [1.04, 12.79], p ≤ .05; unprotected anal sex, OR = 2.73, 95% CI [1.33, 5.60], p ≤ .01; and multiple partners with inconsistent condom use, OR = 2.68, 95% CI [1.42, 5.07], p ≤ .01. The association with any risky sexual behavior was evident for all three types of maltreatment but was strongest for childhood neglect, OR = 2.88, 95% CI [1.74, 4.77], p ≤ .001, and weakest for sexual abuse, OR = 2.46, 95% CI [1.08, 5.62], p ≤ .05. In addition, relationships with specific types of sexual risk taking varied for different types of childhood abuse or neglect. Sexual abuse was associated with unprotected anal sex, OR = 3.11, 95% CI [1.02, 9.45], p ≤ .05. Physical abuse was associated with treatment for an STD, OR = 7.83, 95% CI [1.83, 33.50], p ≤ .01, and multiple partners with inconsistent condom use, OR = 3.35, 95% CI [1.38, 8.15], p ≤ .01. Neglect was associated with unprotected anal sex, OR = 2.88, 95% CI [1.38, 6.01], p ≤ .01, and multiple partners with inconsistent condom use, OR = 2.70, 95% CI [1.40, 5.20], p ≤ .01. It should also be noted that the ORs reflecting the magnitude of relationships between childhood abuse and neglect and trading sex were substantial (3.83–6.04), although they reached only marginal statistical significance, possibly because of reduced power to detect a significant effect due to the small number of individuals who reported this behavior.
Past Year HIV Risk Behavior Among Abused and Neglected Children and Matched Controls Followed Up at Approximate Age 41
Structural Equation Modeling
The measurement model provided an acceptable fit, χ2(49) = 191.12, p < .05, CFI = 0.90, TLI = 0.93, RMSEA = .05, WRMR = 1.14. Factor loadings on the latent constructs were all statistically significant and ranged from .32 to .84 (see Table 1). Bivariate correlations indicated significant direct relationships between childhood abuse and neglect, HIV risk behavior, and each of the four proposed mediators (see Table 3), thereby supporting the initial criteria for mediation. However, the correlation between child abuse and neglect and drug and alcohol use was very small.
Bivariate Correlations Between Variables in Confirmatory Factor Analysis
The results from separate path models with each potential mediator are depicted in Figure 1. Only the risky relationships construct was clearly supported as a mediator. This model explained 13% of the variance in HIV risk behavior, and indirect effects were significant, β = .07, 95% CI [.01, .13]. The other mediators were not strongly supported, based on consideration of statistical significance and magnitude of indirect effects. The model with delinquent and criminal behavior explained 3% of the variance in risky sexual behavior, and indirect effects were small, β = .02, 95% CI [.001, .03]. In this model, the coefficient for the path from delinquent and criminal behavior was small, despite being statistically significant. In the path model with affective symptoms, childhood abuse and neglect predicted increased affective symptoms, but affective symptoms did not significantly predict HIV risk behavior. The indirect effects in this model were small, β = .03, 95% CI [−.003, .06]. In the model with drug and alcohol use, on the other hand, childhood abuse and neglect did not significantly predict drug and alcohol use, although drug and alcohol use did increase the likelihood of HIV risk behavior. In all four models, the direct path between childhood abuse and neglect and HIV risk behavior remained significant, suggesting that risky relationships only partially mediate this relationship.
Figure 1. Separate mediator models predicting HIV risk behavior from childhood abuse and neglect. Numbers in parentheses are bias-corrected bootstrapped 95% confidence intervals around the indirect effect. χ2 = chi-square; df = degrees of freedom; CFI = comparative fit index; TLI = Tucker–Lewis index; RMSEA = root-mean-square error of approximation; WRMR = weighted root-mean-square residual. * p < .05. ** p < .01. *** p < .001.
As recommended by Kraemer et al. (2008), moderated mediation models were examined for affective symptoms and for delinquent and criminal behavior, which were associated with abuse and neglect but not HIV risk behavior in the multivariate models. Inclusion of the interaction between affective symptoms and child abuse and/or neglect in a model with random slopes using a robust maximum likelihood estimator (Klein & Moosbrugger, 2000) did not yield a significant moderation effect for affective symptoms (z = 0.78). Similarly, the interaction of child abuse and neglect with delinquent and criminal behavior was not significant (z = 1.49).
Although risky relationships alone emerged as a significant mediator, we subjected this mediator to a more strenuous test by examining two separate multiple-mediator models, one including delinquent and criminal relationships and the second including affective symptoms. Results from the model with both risky relationships and delinquent and criminal behavior as mediators are depicted in Figure 2. The model provided a good fit, χ2(17) = 53.05, p < .05, CFI = 0.96, TLI = 0.93, RMSEA = .04, WRMR = 0.99, and explained 14% of the variance in HIV risk behavior. Risky relationships remained a significant mediator, when delinquent and criminal behavior was included. The indirect effect through risky relationships was only marginally significant, although the magnitude remained the same as in the single-mediator model, β = .07, 95% CI [−.002, .14]. Delinquent and criminal behavior did not contribute substantially to variance in HIV risk behavior, although risky relationships and delinquent and criminal behavior were moderately correlated with each other.
Figure 2. Path model predicting HIV risk behavior from child abuse and neglect through risky relationships and delinquent and criminal behavior. Numbers in parentheses are bias-corrected bootstrapped 95% confidence intervals around the indirect effect. χ2 = chi-square; df = degrees of freedom; CFI = comparative fit index; TLI = Tucker–Lewis index; RMSEA = root-mean-square error of approximation; WRMR = weighted root-mean-square residual. * p < .05. ** p < .01. *** p < .001.
Findings for the model including both risky relationships and affective symptoms as mediators (see Figure 3) were similar. The model provided a strong fit, χ2(14) = 12.20, p > .05, CFI = 1.00, TLI = 1.00, RMSEA = .00, WRMR = 0.46, and explained 12% of the variance in HIV risk behavior. In this model, the path from risky relationships to HIV risk behavior was only marginally significant, although the magnitude of the relationship was consistent, and the indirect effect decreased. However, the relationship between affective symptoms and HIV risk behavior, as well as associated indirect effects, was close to zero. In both multiple-mediator models, the direct relationship between child abuse and neglect and HIV risk behavior remained significant.
Figure 3. Path model predicting HIV risk behavior from child abuse and neglect through risky relationships and affective symptoms. Numbers in parentheses are bias-corrected bootstrapped 95% confidence intervals around the indirect effect. χ2 = chi-square; df = degrees of freedom; CFI = comparative fit index; TLI = Tucker–Lewis index; RMSEA = root-mean-square error of approximation; WRMR = weighted root-mean-square residual. ** p < .01. *** p < .001.
DiscussionThe first notable finding from this study is that individuals with documented cases of childhood abuse and neglect reported increased HIV risk behavior in middle adulthood, 30 years after these childhood experiences. Documentation of increased sexual risk taking in these abused and neglected individuals followed up in middle adulthood extends findings with the same sample in young adulthood and adds validity to the larger body of research indicating correlations between sexual risk behavior and adult retrospective reports of childhood maltreatment. Victims of childhood abuse and neglect appear to be at risk for a long-term pattern of health-compromising sexual behaviors that extends into middle adulthood, when risky sexual behavior decreases for most individuals. In our sample overall, 13% reported sexual risk taking in middle adulthood, but 17% of individuals with histories of childhood abuse or neglect reported sexual risk, and this rate was a nearly threefold increase over that of the controls. Our findings add to increasing recognition that the long-term consequences of childhood abuse and neglect extend to physical health risk, and they underscore the importance of clinical interventions to reduce sexual risk taking among victims of childhood abuse and neglect.
In addition to documenting increased sexual risk behavior, findings from this study shed light on mechanisms that may explain the link from childhood abuse and neglect to sexual risk. Of the set of potential mediators assessed (risky relationships, affective symptoms, drug and alcohol use, and delinquent and criminal behavior), risky relationships emerged as the most powerful factor linking child abuse and neglect to risky sexual behavior. Thus, risky sexual behavior appears to take place in the context of generally chaotic, unstable relationships characterized by disruptions, sexual infidelity, and lack of monogamy. These problematic relationship patterns may develop as a result of disrupted early attachment (Main, 1996) as well as the neurobiological effects of abuse and neglect (De Bellis, 2001; Glaser, 2000). It is important to note that the data available in this study reflect participant reports about their own behavior rather than their partners' behavior or level of risk. Other research suggests that involvement with risky partners may largely explain increased risk for STDs among women with histories of sexual abuse (Testa, VanZile-Tamsen, & Livingston, 2005). In addition, the indicators of relationship risk included in this study may not necessarily reflect abnormal or maladaptive relationships (e.g., multiple marriages). Taken together, however, the construct reflects a pattern of romantic relationships lacking stability or commitment. Moreover, risky relationships were associated with delinquent and criminal behavior, suggesting that these relationship characteristics are associated with a general pattern of risky, deviant behavior.
The relationship between delinquent and criminal behavior in young adulthood and HIV risk behavior in middle adulthood was tenuous, and this pathway was no longer significant when risky relationships were included in the model. As noted above, however, risky relationships were associated with delinquent and criminal behavior. Thus, it may be that these relationship characteristics develop as part of a larger pattern of antisocial behavior among victims of child abuse and neglect. Other analyses with this sample, which did not include relationship risk, have emphasized the role of antisocial behavior as a mediator in the link to risky sexual behavior (Wilson & Widom, 2008b, 2010a). Indeed, several of the items used to assess relationship risk were drawn from a measure of antisocial personality disorder. Nonetheless, it appears that deviance in the context of romantic relationships may provide the direct link to risky sexual behavior and may even mediate the pathway from general antisocial behavior to sexual risk taking. Because both general delinquency and relationship risk were assessed at the same point in time in this study, we could not directly test the more complex mediational pathway, but evidence of a connection between the two constructs provides some support for this hypothesis.
In this study, neither affective symptoms nor drug and alcohol use was supported as a mediator of the relationship between childhood maltreatment and risky sexual behavior. Lack of a strong connection between child abuse and neglect and substance use in young adulthood is consistent with other findings from this sample (Widom, Weiler, & Cottler, 1999). In this sample, relationships between child maltreatment and substance use do not emerge until middle adulthood and exist primarily for women (Widom et al., 2006). Nonetheless, drug and alcohol use did appear to increase HIV risk sexual behavior for the sample overall. Moreover, it is possible that greater drug use in middle adulthood among women who experienced abuse and neglect (Wilson & Widom, 2009a) contributes to sexual risk taking at this time point.
An opposite pattern was revealed for affective symptoms, which were associated with childhood abuse and neglect but were not strongly linked to sexual risk. Although symptoms of depression and PTSD have been linked to sexual risk in other studies (Mazzaferro et al., 2006; Swanholm, Vosvick, & Chng, 2009), these problems in young adulthood did not predict later sexual risk behavior in our sample. As with delinquent and criminal behavior, affective symptoms were associated with risky relationships and may contribute to risk for involvement in unstable, chaotic relationships. However, affective symptoms in young adulthood may not necessarily persist into middle adulthood or directly influence behavior in middle adulthood. Thus, correlations found between affective symptoms and sexual risk behavior at a single point in time may not generalize to the longitudinal relationship assessed in this study. Furthermore, findings from another recent study suggest that although there are correlations between these phenomena, depression may not be involved in the relationship between child abuse and sexual risk behavior (Morokoff et al., 2009).
This study had several advantages. First, the prospective longitudinal design allowed for determination of the correct temporal sequence in the variables of interest. Second, unlike most studies, which end at adolescence or young adulthood, this study traced development into middle adulthood. Third, the sample is large, includes men and women, and is ethnically diverse. Fourth, documented cases of childhood maltreatment minimize potential problems with reliance on retrospective self-reports and provide a nonambiguous definition of childhood abuse and neglect. Fifth, we examined mediating mechanisms to uncover specific processes that may explain this relationship.
Despite the strengths of this study, a number of important limitations must be noted. First, although the use of documented cases of child abuse and neglect is an advantage, this means that only cases that came to the attention of authorities and met the threshold for a legal definition of abuse and neglect were included. Less severe or unreported cases were not reflected. Second, our measure of risky relationships reflects participants' own behavior, rather than characteristics of participants' partners. Third, we examined only a subset of the possible mediators that may explain this relationship. Fourth, the base rate of HIV risk behavior in middle adulthood was fairly low, and this may have reduced power. Finally, cases of abuse and neglect occurred in the late 1960s and early 1970s in a midwestern metropolitan area in the United States, and therefore our results may not generalize to all cases of child maltreatment.
Results of this study draw attention to the potential long-term consequences of child abuse and neglect for physical health, particularly sexual risk. Findings also point to romantic relationships as an important focus of intervention and prevention efforts for reducing HIV risk behavior among victims of childhood abuse and neglect. Helping victims of abuse and neglect to form healthy romantic relationships early in life may reduce risky sexual behavior that persists into middle adulthood.
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Submitted: June 22, 2010 Revised: November 17, 2010 Accepted: December 20, 2010
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Source: Journal of Consulting and Clinical Psychology. Vol. 79. (2), Apr, 2011 pp. 236-246)
Accession Number: 2011-04114-001
Digital Object Identifier: 10.1037/a0022915
Record: 38- Title:
- Patterns of pregnancy and postpartum depressive symptoms: Latent class trajectories and predictors.
- Authors:
- Fredriksen, Eivor, ORCID 0000-0002-3442-4480. Department of Psychology, University of Oslo, Oslo, Norway, eivor.fredriksen@psykologi.uio.no
von Soest, Tilmann. Department of Psychology, University of Oslo, Oslo, Norway
Smith, Lars. Department of Psychology, University of Oslo, Oslo, Norway
Moe, Vibeke. Department of Psychology, University of Oslo, Oslo, Norway - Address:
- Fredriksen, Eivor, Department of Psychology, University of Oslo, P.O. Box 1094 Blindern, 0317, Oslo, Norway, eivor.fredriksen@psykologi.uio.no
- Source:
- Journal of Abnormal Psychology, Vol 126(2), Feb, 2017. pp. 173-183.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- postpartum depression, perinatal depression, maternal dysphoria, growth mixture modeling
- Abstract (English):
- Depressive symptoms among pregnant and postpartum women are common. However, recent studies indicate that depressive symptoms in the perinatal period do not follow a uniform course, and investigations of the heterogeneity of time courses and associated factors are needed. The aim of this study was to explore whether depressive symptoms in the perinatal period could be categorized into several distinct trajectories of symptom development among subgroups of perinatal women, and to identify predictors of these trajectory groups. The study used data from 1,036 Norwegian women participating in a community-based prospective study from midpregnancy until 12-months postpartum. Depressive symptoms were assessed with the Edinburgh Postnatal Depression Scale at 7 time points (4 during pregnancy). Partner-related attachment, stress, childhood adversities, pregnancy-related anxiety, previous psychopathology, and socioeconomic conditions were assessed at enrollment. By means of growth mixture modeling based on piecewise growth curves, 4 classes of depressive symptom trajectories were identified, including (a) pregnancy only (4.4%); (b) postpartum only (2.2%); (c) moderate-persistent (10.5%); and (d) minimum symptoms (82.9%) classes. Multinomial logistic regression analyses showed that membership in the pregnancy only and postpartum only classes primarily was associated with pregnancy-related anxiety and previous psychopathology, respectively, whereas the moderate-persistent class was associated with diverse psychosocial adversity factors. Findings suggest heterogeneity in temporal patterns of elevated depressive mood, relating specific trajectories of time courses with distinct adversity factors. Researchers and clinicians should be aware of possible multiple courses of elevated perinatal depressive mood, and inquire about possible diverse adversity factors, aberrant pathways, and prognoses. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Impact Statement:
- General Scientific Summary—This study suggests that depressive symptoms during pregnancy and the postpartum period do not follow a uniform course, but rather supports a model of several distinct time courses of depressed mood associated with diverse psychosocial adversity factors. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Major Depression; *Perinatal Period; *Postpartum Depression; *Pregnancy; Mothers
- PsycINFO Classification:
- Affective Disorders (3211)
- Population:
- Human
Female - Location:
- Norway; US
- Age Group:
- Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs) - Tests & Measures:
- Experiences in Close Relationships Scale
Parenting Stress Index--Norwegian Version
Adverse Childhood Experiences Scale
Pregnancy-Related Anxiety Questionnaire--Revised DOI: 10.1037/t57856-000
Edinburgh Postnatal Depression Scale DOI: 10.1037/t01756-000
Parenting Stress Index DOI: 10.1037/t02445-000 - Grant Sponsorship:
- Sponsor: Research Council of Norway, Norway
Grant Number: 196156
Recipients: No recipient indicated - Methodology:
- Empirical Study; Longitudinal Study; Quantitative Study
- Supplemental Data:
- Tables and Figures Internet
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Dec 1, 2016; Accepted: Nov 4, 2016; Revised: Nov 4, 2016; First Submitted: May 6, 2016
- Release Date:
- 20161201
- Correction Date:
- 20170323
- Copyright:
- American Psychological Association. 2016
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/abn0000246; http://dx.doi.org.offcampus.lib.washington.edu/10.1037/abn0000246.supp(Supplemental)
- PMID:
- 27935730
- Accession Number:
- 2016-58118-001
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2016-58118-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2016-58118-001&site=ehost-live">Patterns of pregnancy and postpartum depressive symptoms: Latent class trajectories and predictors.</A>
- Database:
- PsycINFO
Patterns of Pregnancy and Postpartum Depressive Symptoms: Latent Class Trajectories and Predictors
By: Eivor Fredriksen
Department of Psychology, University of Oslo and National Network for Infant Mental Health in Norway, Centre for Child and Adolescent Mental Health, Eastern and Southern Norway, Oslo, Norway;
Tilmann von Soest
Department of Psychology, University of Oslo
Lars Smith
Department of Psychology, University of Oslo
Vibeke Moe
Department of Psychology, University of Oslo and National Network for Infant Mental Health in Norway, Centre for Child and Adolescent Mental Health, Eastern and Southern Norway
Acknowledgement: This research was supported by grant 196156 from the Research Council of Norway. There has been no prior dissemination of the data or ideas appearing in this article.
Postpartum depression (PPD) is one of the most common concomitants of childbirth, and with the accompanying risk of adverse consequences on maternal mental health, child development, and family functioning (Goodman et al., 2011; Meltzer-Brody & Stuebe, 2014), it has been of interest to both clinicians and researchers for decades. More recently, findings indicate that timing and duration of depressive symptoms in the perinatal period do not follow a uniform course, suggesting considerable heterogeneity in symptom trajectories as well as associated antecedents (Cents et al., 2013; Mora et al., 2009; PACT Consortium, 2015; Sutter-Dallay, Cosnefroy, Glatigny-Dallay, Verdoux, & Rascle, 2012; Wisner et al., 2013). Investigating differential patterns of depressive symptomatology may enable efforts to develop more personalized approaches to treatment and prevention (Cuijpers et al., 2012). Exploring differential time courses and associated predictors may further provide a basis for investigating possible diverse etiologies, outcomes, and long-term prognoses.
This study employed a large, multisite community-based sample (N = 1,036) with seven data collection waves to examine trajectories of depressive symptoms from early pregnancy to 1-year postpartum with a dimensional approach. We examined whether subgroups of women following distinct trajectories of depressive symptoms can be identified, and whether a range of psychosocial adversity factors supposed to be risk factors for PPD can predict class membership.
Time Course of Depressive Symptoms Across Pregnancy and the Postpartum PeriodThere is an ongoing debate about the temporal definition of symptom onset in PPD (PACT Consortium, 2015; Wisner, Moses-Kolko, & Sit, 2010). Some emphasize the elevated incident rates during the first few weeks after birth, suggesting a narrowly defined period for symptom onset. This early elevated risk has been connected with physiological and psychological changes in the first postpartum weeks and suggested to constitute a specific phenotype (Forty et al., 2006; Munk-Olsen, Laursen, Pedersen, Mors, & Mortensen, 2006). Others have expanded the time frame of PPD up to 1-year postpartum (O’Hara & McCabe, 2013). With a broadened time frame, PPD has been understood as a continuation of earlier mental health problems (Patton et al., 2015). Further, there is a growing number of reports highlighting the importance of investigating onset of depression during pregnancy, as well as depression limited to the pregnancy period (Pearson et al., 2013). For example, a study addressing the heterogeneity of PPD found that among women with the most severe subtype of PPD, the majority had a pregnancy onset (67%). In less severe subtypes of PPD, pregnancy onset was rarer (11% and 34%; PACT Consortium, 2015). Moreover, in a study screening 10,000 women, Wisner et al. (2013) found that among screen-positive cases only 40% of women’s depressive episodes began postpartum, while 33% had a pregnancy onset. There is also considerable variation regarding the duration of PPD; for most women diagnosed with PPD it seems to be a time-limited condition, whereas for a substantial subgroup (38%) depression develops into a persistent disorder (Vliegen, Casalin, & Luyten, 2014).
Only two studies have investigated heterogeneous time courses of depressive symptoms in a time frame limited to pregnancy and the postpartum period (Mora et al., 2009; Sutter-Dallay et al., 2012). Mora et al. (2009) found three groups with transient courses with high level symptoms predominantly (a) during pregnancy, (b) early postpartum, and (c) late postpartum, respectively. Additionally, they identified stable classes with (d) low symptom levels and (e) a chronic high trajectory. With a somewhat smaller sample Sutter-Dallay, Cosnefroy, Glatigny-Dallay, Verdoux, and Rascle (2012) described similar classes; however, they did not find specific postpartum classes reaching clinical levels. Several studies have investigated the heterogeneity of maternal depressive symptom trajectories from the perinatal period into childhood years (Campbell, Matestic, von Stauffenberg, Mohan, & Kirchner, 2007; Cents et al., 2013; Luoma, Korhonen, Salmelin, Helminen, & Tamminen, 2015; Matijasevich et al., 2015; van der Waerden et al., 2015). All studies report several classes, ranging from four to six, with distinct trajectories, suggesting that a singular model of symptom onset and course is unwarranted. A robust finding across studies is that most women follow trajectories of minimal or mild symptoms. Further, all studies found a small class with a chronic high symptom burden. Stable trajectories (at different levels of severity) were common, whereas various transient trajectories tended to comprise relatively smaller class proportions. The findings are in accordance with research on heterogeneous courses in the transition to parenthood in concepts such as life satisfaction, where most participants report stable levels, although small subgroups show increasing or decreasing trajectories (Galatzer-Levy, Mazursky, Mancini, & Bonanno, 2011).
However, of studies spanning the perinatal period into childhood years, only one found a pregnancy-only class (van der Waerden et al., 2015), and none reported specific postpartum classes. None of these studies had more than one measurement point during pregnancy, and several had none. In most of the studies, the majority of measurement points was after the postpartum period had passed. By including measurement points outside the postpartum period, there is a danger of missing mood changes specific for this period, because trends more typical of maternal mood at later stages may disguise fine grained developmental trends that may be found particularly in this period. To be able to capture the specific mood changes of the pregnancy and postpartum period, it is useful to apply a limited time frame with enough measurement points (Ram & Grimm, 2007). The present work extends extant studies by including several measurement points during pregnancy, by limiting the time period to pregnancy and 1-year postpartum, and building a statistical model suited to detect shorter-term changes in symptom levels in close proximity to childbirth.
Risk Factors of Depressive Symptoms in Pregnancy and the Postpartum PeriodReviews of risk factors for perinatal depression include previous psychopathology, domestic violence, history of abuse, life stress, lack of social or partner support, migration status, and anxiety during pregnancy as robust risk factors across studies. Pregnancy complications, neuroticism, family history of psychiatric illness, low socioeconomic status, substance misuse, and chronic illness are listed as risk factors with slightly less systematic evidence (Biaggi, Conroy, Pawlby, & Pariante, 2016; Howard et al., 2014; O’Hara & McCabe, 2013). The extent to which the same risk factors predict various trajectories of depressive symptoms in the perinatal period has received less attention. In studies investigating differential courses of depressive symptoms in this period sociodemographic variables, anxiety, stress, previous psychopathology, lack of social support, poor relationship quality, and minority status predicted class membership in subgroups with increased symptom burden relative to subgroups with minimal symptoms (Cents et al., 2013; Luoma et al., 2015; Mora et al., 2009; Sutter-Dallay et al., 2012; van der Waerden et al., 2015). Moreover, a review investigating differences between chronic and transient courses of PPD found that poor partner relationship, life stress, contextual risk, personality factors, and to some extent childhood abuse and low maternal care were associated with chronic time courses of PPD, relative to remitting time courses (Vliegen et al., 2014).
This study builds on and extends these findings in several ways. Measures of previous psychopathology, partner-related attachment patterns, life stress, pregnancy-related anxiety, childhood trauma, and sociodemographic variables are included as predictor variables. These constitute important risk factors across several contexts; however, less is known of how these specific factors are related to various depressive symptom trajectories in the perinatal period. Specifically, this study extends earlier research on partner relations by including a measure of partner-related attachment patterns. Partner-related attachment has received little attention in research on perinatal depression; however, insecure attachment styles have been related to a diagnosis of PDD (Ikeda, Hayashi, & Kamibeppu, 2014). Further, ambivalent attachment styles predicted increases in depressive symptoms from pregnancy to the postnatal period (Simpson, Rholes, Campbell, Tran, & Wilson, 2003). Moreover, instead of applying a general measure of anxiety; this study assessed pregnancy-related anxiety, because including features of the perinatal period is central to the idea of the study. Pregnancy-related anxiety is considered to constitute a distinct clinical entity with the capacity of predicting birth outcome independently of more generalized symptom measures, as well as explaining unique variance in postnatal mood disturbance (Blackmore, Gustafsson, Gilchrist, Wyman, & O’Connor, 2016; Huizink, Mulder, Robles de Medina, Visser, & Buitelaar, 2004). Finally, by including childhood trauma as a predictor, this study relates to research showing an increased risk of PPD among women with a history of abuse (Howard et al., 2014), and extends this literature by including a broad range of childhood adversities.
Study Aims and HypothesesIn this study, we investigated maternal depressive symptoms with a dimensional approach within a large multisite community-based sample of women at seven time points from pregnancy through 12-months postpartum. The first aim was to explore whether maternal depressive symptoms throughout this period could be categorized into several distinct, empirically defined trajectories. Based on extant literature we expected (a) one trajectory characterizing women with elevated symptoms limited to the pregnancy period (Mora et al., 2009; Pearson et al., 2013); (b) one trajectory of early postpartum onset and a gradual recovery, based on findings of increased incidence early postpartum (Munk-Olsen et al., 2006) and studies of heterogeneous trajectories (Mora et al., 2009); (c) a stable trajectory at a moderate level with pregnancy onset in which symptoms continue into the postpartum period (PACT Consortium, 2015; Wisner et al., 2013); (d) a small group of women with a very high symptom level throughout the period of study, as this has been a consistent finding in studies of heterogeneous courses (Cents et al., 2013; Mora et al., 2009; van der Waerden et al., 2015); and (e) a majority of women presenting minimum symptoms (Cents et al., 2013; Mora et al., 2009; van der Waerden et al., 2015).
Second, we aimed to investigate whether potential psychosocial adversity factors, such as sociodemographic factors, previous psychopathology, stress, partner-related attachment patterns, pregnancy-related anxiety, and childhood trauma were differentially associated with the hypothesized trajectories. More specifically, we expected that higher levels of adversity predicted membership in trajectory classes with elevated symptom burden, relative to trajectories with low symptoms. Further, we expected stable courses with elevated symptoms to be predicted by more adversity factors than transient courses, as it has been shown that persistent courses of PPD are characterized by higher levels of adversities than time-limited courses (Vliegen et al., 2014).
Method Procedure and Participants
This study is based on data from 1,036 women participating in the prospective multisite Little in Norway study (Moe & Smith, 2010). From September 2011 until October 2012, all pregnant women receiving routine prenatal care at nine public well-baby clinics in Norway were invited to participate in the study. Initially 1,041 women consented to participate; five women later withdrew their consent, leaving 1,036 (99.5%) women as participants. There were no exclusion criteria. At five clinics, the staff did not establish reliable routines to monitor rates of participation. At the remaining four clinics 50.7% of all women attending the clinic consented to participate. Participation rates were probably similar at the other five sites because recruitment strategies and resources allocated to the data collection were similar at all well-baby clinics. Comparisons of educational level of this sample with official national statistics of Norwegian women of similar age and residential area showed that participants in the study had a significantly higher educational level (Statistics Norway, 2014). This study uses data from seven time points: at average gestational Week 21 (range: weeks 8–34, T1); Week 28 (T2); Week 32 (T3); and Week 36 (T4); 6-weeks postpartum (T5); 6-months postpartum (T6); and 12-months postpartum (T7). Participants were recruited at their first prenatal care examination at the well-baby clinics. There is considerable variation in local and individual practices as to when pregnant women first receive prenatal care at a well-baby clinic (many choose to receive initial checkups at their general practitioner). As a result, the time frame for enrollment was rather large (i.e., between gestational week 8 and 34), and a comprehensive number of participants missed the early data collection points. Thus, the recruited numbers of participants at T1 and T2 were n = 659 and 579, respectively. Response rates at T2 were considerably lower than at other time points due to shortage of staff members to collect data. Response rates at T7 were also lower, reflecting the fact that paid parental leave ends one year after birth in Norway, and parents are returning to work. Information about recruitment and response rates is depicted in Figure 1.
Figure 1. Recruitment and response rates (N = 1,036). There is considerable variation in local and individual practices as to when pregnant women first receive prenatal care at a well-baby clinic and, consequently, were recruited to participate. As a result, the time frame for enrollment is wide (varying from gestational week 8 to 34), and a considerably number of participants missed the early data collection points. This resulted in reduced participant numbers at T1 (n = 659) and T2 (n = 579).
Data were collected digitally by means of web-based questionnaires at all time points. Primarily, responses were submitted at designated computers at the well-baby clinics. However, at T3 and T4, respondents were asked to complete the questionnaire at their private computers at home. The nine well-baby clinics were located at geographically diverse sites across Norway.
Attrition analyses were conducted by means of univariate logistic regression analyses and showed that lower education (OR = 0.93, 95% CI [0.87, 0.99], p = .02); parity (OR = 0.75, 95% CI [0.57, 0.99], p = .04); and childhood trauma (OR = 1.20, 95% CI [1.06, 1.37], p < .01) predicted dropout at T7. Age, previous psychopathology, partner-related attachment, life stress, and pregnancy-related anxiety did not show any significant associations with missing status (p > .05). Further, high levels of depressive symptoms T1 to T5 significantly predicted dropout (ORs = 1.06–1.10, p < .05), whereas depressive symptoms at T6 were not predictive (OR = 1.05, 95% CI [1.00, 1.10], p = .06).
At enrollment the mean age of the participants was 30.3 years (range: 17–43, SD = 4.8), 54.9% of the women were nulliparous. Most women were married (36.2%) or cohabiting (59.7%), with only a small fraction being single/divorced/separated (2.7%), or not specifying their marital status (1.4%). A large proportion of participants was educated at university level (77.1%), while the highest completed education of the remaining participants was high school level (19.8%) or lower (3.1%). At enrollment, 77.3% of the participants were full-time employed, 5.8% full-time students, 13.6% part-time students/part-time employed, while 3.0% reported being unemployed/on benefits/homemakers. Median annual personal income ranged from the equivalent of $36,000–$55,000 (44.4%), while 31.1% had lower and 24.3% higher income. The ethnic majority was Norwegian (93.9%), with a few reporting a diversity of other ethnic backgrounds (6.1%).
Measures
With the exception of measures of depressive symptoms, which were assessed at all seven data collection points, all measures described below were collected at enrollment.
Depressive symptoms
Maternal depressive symptoms were assessed using the Edinburgh Postnatal Depression Scale (EPDS), originally developed to screen for depressive symptoms in women in the postpartum period (Cox, Holden, & Sagovsky, 1987), and later validated for antepartum use (Murray & Cox, 1990). The EPDS is a 10-item self-report questionnaire asking respondents to consider various depressive symptoms during the last 7 days on a 4-point scale (range: 0–30). Although developed with cut-off scores indicating probable depression, the EPDS composite score has also been used as a continuous variable for research purposes (Matijasevich et al., 2015), with the benefit of yielding a more detailed range of depressive symptomatology at both clinical and subclinical levels. The EPDS composite score was used as a continuous variable in this study. Cronbach’s alphas were high at each assessment (ranging from .80 to .85), indicating good internal consistency.
Sociodemographic factors
Education was stipulated in years of education. Parity was assessed by asking participants to state number of previous children, and was coded as a dichotomous variable (nulliparous/multiparous).
Previous psychopathology
Participants were asked the following question: “Have you ever experienced mental health problems earlier in life? (yes/no).” Similar single question measures have been shown to serve as acceptable screeners for mental health problems (Veldhuizen, Rush, & Urbanoski, 2014), and have previously been used extensively in research (van der Waerden et al., 2015).
Partner relationship
Characteristics of partner relationship were assessed by the Experiences in Close Relationships Scale (ECR), which is a 36-item self-report measure of adult romantic attachment styles rated on a 7-point scale. ECR yields two subscales of underlying attachment: anxiety (fear of interpersonal rejection or abandonment, an excessive need for approval from others, and distress when one’s partner is unavailable or unresponsive), and avoidance (fear of dependence and interpersonal intimacy, an excessive need for self-reliance, and reluctance to self-disclose; Brennan, Clark, & Shaver, 1998). Higher scores reflect greater levels of insecure attachment within each relationships domain (range 18–126 on each subscale). In this study Cronbach’s alphas were .88 and .89 for anxiety and avoidance subscales, respectively, in accordance with the high level of internal consistency reported in other studies (Brennan et al., 1998).
Stressful life events
Stress was measured by the life stress subscale, which is part of The Parenting Stress Index (PSI; Abidin, 1995). The Norwegian version of the subscale lists 22 major life events (Kaaresen, Ronning, Ulvund, & Dahl, 2006), such as serious illness in the family, changing school or work place (range 0–91). The respondents are asked to indicate whether the family had experienced each of the life events during the last 12 months. Items were weighted according to the Professional Manual of the Parenting Stress Index (Abidin, 1995), and the composite score was used in this study.
Anxiety during pregnancy
Anxiety related to pregnancy and birth was assessed by the 10-item Pregnancy Related Anxiety Questionnaire—Revised (PRAQ-R; Huizink et al., 2004). Each item is measured on a 5-point scale. PRAQ-R yields three subscales (fear of giving birth, fear of bearing a physically or mentally handicapped child, and concerns about one’s own appearance). In this study, mean scores across all 10 items were computed to obtain an indication of overall level of anxiety related to pregnancy and birth (Cronbach’s alpha = .84, range: 10–50).
Childhood trauma
Childhood traumas were assessed retrospectively by the Adverse Childhood Experiences Scale (ACE), a self-report measure of childhood abuse, neglect, and household dysfunction (Dong et al., 2004). It lists 10 types of adverse childhood experiences and asks whether they have been experienced during their childhood. ACE has shown good test–retest reliability (Dong et al., 2004). Dong et al. (2004) showed that experiencing one type of adverse childhood event increased the odds of having additional adverse childhood experiences, and highlighted the importance of looking at the extent of such experiences rather than effects of a specific type. In this study we used the sum of reported types, ranging from 0 to 10.
Statistical Analysis
Statistical analyses were conducted in two steps. First, the time course of depressive symptoms from midpregnancy through 1-year postpartum was modeled, and subgroups of women with distinct longitudinal courses of depressive symptoms were identified. For this purpose, latent growth curves (LGC) were modeled based on EPDS composite scores at all seven time points (Bollen & Curran, 2006). To represent birth as a major event, a linear three-piece piecewise growth curve model was estimated (Flora, 2008) with the first transition point at the end of the pregnancy period (i.e., T4) and the second 6 weeks after birth (i.e., T5). The three-piece model yielded three phases of symptom development: a pregnancy phase, a peripartum phase, and a postpartum phase. By allowing for sharp transitions at these specific time points the statistical model was able to represent the theoretical expectation of differential change rates during these phases (Ram & Grimm, 2007), such as a pattern of rapid change in symptom levels during the peripartum phase and relatively slower change during the pregnancy and postpartum phases. Two-piece models with only one transition point at either T4 or T5 were also modeled to examine whether the three-piece model with its capacity of detecting slopes with rapid change in close proximity to birth in fact showed superior fit compared with growth models that did not allow for such patterns. Based on these growth curves, latent growth trajectory classes were estimated by means of growth mixture modeling (GMM; Muthén, 2004). GMM can account for heterogeneity in longitudinal patterns of depressive symptomatology as latent classes correspond to qualitatively distinct trajectories. Variances were constrained to be equal across classes, as convergence issues emerged when models with unique variances across classes were estimated.
Second, class membership was regressed on the potential psychosocial adversity factors by estimating multinomial logistic regression models using the three-step modal ML approach accounting for class assignment uncertainties (Asparouhov & Muthén, 2014; Vermunt, 2010). This was done to examine the association of possible predictor variables with class probabilities. Such associations were initially investigated with a univariate approach and subsequently with a multivariate approach to reach the most robust set of predictor variables. Only significant predictors (p < .05) from the univariate analyses were entered in the multivariate model. The scales of the continuous predictor variables were z-transformed, to make them more readily comparable (with the exception of age and education which were measured in years).
Model fit of basic growth models was evaluated by inspecting χ2-square statistics, Confirmatory Fit Index (CFI), Tucker–Lewis Index (TLI), and the root mean square error of approximation (RMSEA). According to recommendations in the literature, CFI and TLI values of .95 or greater and RMSEA values of .06 or lower are considered as indicating good fit (Hu & Bentler, 1999). To decide on number of classes, the bootstrapped likelihood ratio test (BLRT), the Lo–Mendell–Rubin adjusted likelihood ratio test (LMR-LRT), and the Bayesian information criteria (BIC)/sample size adjusted BIC (SABIC) were used (Nylund, Asparouhov, & Muthén, 2007). Entropy values, which represent the quality of classification of individuals into latent classes, were also inspected. Finally, overall interpretability was evaluated, and we excluded models with classes comprising less than 20 women.
Missing data were handled by the full information maximum likelihood procedure (FIML) accounting for missing at random (MAR) assumptions. Moreover, because missing data due to dropout in longitudinal studies may not fulfill MAR assumptions, we additionally tested models handling dropout that is not missing at random (NMAR; Muthén, Asparouhov, Hunter, & Leuchter, 2011). All data analyses were performed in Mplus 7.3, using maximum likelihood estimation with robust standard errors (Muthén & Muthén, 2015).
ResultsMeans, standard deviation, and a correlation matrix of all variables used in the study are presented in Table 1. The table shows mean EPDS scores to range from 2.88 to 4.54, well below clinical cut-off, with generally higher EPDS mean levels during pregnancy compared with the postpartum period. Correlations between assessments were moderate to high (.40 ≤ r ≤ .72) and followed a pattern of stronger correlations among assessments closer in time. The means of life stress index (7.08), ECR anxiety and avoidance (44.23 and 30.05, respectively), PRAQ (22.70), and ACE (0.75) were at the lower end of the scales, which would be expected in a community-based sample.
Means, Standard Deviations, and Correlation Matrix of All Measures
Latent Growth Curve Models
LGC models were fitted based on EPDS mean scores at all seven time points. Initially, a basic model allowing for linear development across all time points with an intercept (estimated initial status early in pregnancy) and a slope (estimated change in depressive symptoms) was estimated. The parameterization of the slope was coded as the number of weeks that had passed since the first measurement, thus reflecting the uneven time intervals between measurements. The basic LGC model had a mean intercept (I) of 4.65 (p < .01) and a mean slope (S) of −0.03 (p < .01), indicating an estimated mean score of depressive symptoms of 4.65 at T1 and a decrease of 0.03 scores each week. However, model fit was poor, χ2(23) = 188.04; CFI = 0.860; TLI = 0.872; RMSEA = 0.083; 90% CI [0.072, 0.094]. A linear three-piece LGC model with transition points at the end of the pregnancy period and 6-weeks postpartum was then estimated (Means: I = 4.38, p < .01; S1 [pregnancy slope] = 0.02, p < .05; S2 [peripartum slope] = −0.10, p < .01; S3 [postpartum slope] = −0.02, p < .01), yielding excellent fit, χ2(14) = 27.00; CFI = 0.989; TLI = 0.983; RMSEA = 0.030; 90% CI [0.012, 0.047]. Results indicate an estimated EPDS mean score of 4.38 at T1, with a slight symptom increase of 0.02 scores each week during pregnancy, a sharper weekly decrease of −0.10 scores in the peripartum phase, with a continued but small weekly decrease of −0.02 scores in the postpartum phase.
Two two-piece models with transition points at the end of pregnancy or 6-weeks postpartum, respectively, were also estimated to investigate if more parsimonious models would yield equivalent fit. However, both models yielded a poor fit, χ2(19) = 173.23; CFI = 0.869; TLI = 0.855; RMSEA = 0.089; 90% CI [0.077, 0.101]; and χ2(19) = 157.35; CFI = 0.882; TLI = 0.870; RMSEA = 0.084; 90% CI [0.072, 0.096]. The linear three-piece model was thus selected for further analyses as it was in accordance with the a priori theoretical model and yielded the best fit.
Growth Mixture Modeling
Next, a series of GMM models was fitted to the three-piece piecewise LGC model for assessment of the optimal number of classes. As Table 2 shows, the two- and three-class solutions were not optimal, as all the fit indices indicated that more classes yielded a better fit. Nor did the six-class solution seem to be adequate as the LMR-LRT indicated fewer classes and the solution included two classes with less than 10 women in each. It was less clear whether a four-class or a five-class solution yielded the best fit, and as neither the BLRT nor the BIC/SABIC provided conclusive answers, we based our decision on LMR-LRT, overall interpretability, and entropy values. LMR-LRT and the entropy values both favored the four-class solution. An inspection of these two solutions showed that the five-class solution in most part reflected the four-class solution, with the exception of one new class (6%) characterized by a high initial level, rapidly dropping to stable low levels. Taking all these aspects into consideration, a four-class solution was finally decided upon. The entropy value was .89 for this model, which indicates good separation of latent classes (Celeux & Soromenho, 1996).
Fit of Growth Mixture Models
Estimated trajectories of the four-class model are depicted in Figure 2, with corresponding parameters found in Table 3. As depicted in Figure 2, the pregnancy-only class (4.4%) represents a heightened initial symptom level early in pregnancy with a steep increase of symptoms during pregnancy, peaking at the last time point before delivery. The symptom level then rapidly dropped during the peripartum period with a continued downward trend postpartum. The postpartum-only class (2.2%) closely resembles the traditional PPD pattern, with low levels during pregnancy, a rapid peripartum onset of symptoms, followed by a gradual postpartum decrease, reaching low symptom levels at the end of the first postpartum year. A third class termed moderate-persistent (10.5%), showed elevated symptoms at a subclinical level with a flat trend during pregnancy. The symptom level dropped slightly during the peripartum period; however, this pattern was reversed in the postpartum period with a steady increase of symptoms the first year after childbirth. The majority of women (82.9%) were categorized into a minimum symptoms class, characterized by low levels of depressive symptoms during pregnancy and with slight, but significant declines after birth.
Figure 2. Estimated mean trajectories of the GMM Four-class model of depressive symptoms from pregnancy to 12-months postpartum.
Parameters of the Four-Class Growth Mixture Model
Because conventional GMM models are based on MAR assumptions, additional analyses under NMAR assumptions were modeled as well. More specifically, we reran our models in the framework of Diggle-Kenward selection model, Roy latent dropout pattern mixture modeling, and Muthén-Roy modeling with latent subgroups of subjects with respect to the piecewise LGC model and the GMM model (Muthén et al., 2011). The estimated parameters, as well as number and proportions of classes did not differ substantially from those in the original models; thus only results from the GMM model under MAR assumptions are reported.
Predictors of Membership in Latent Trajectory Classes
In the next analytic step, the associations of sociodemographic factors, stress, partner attachment, pregnancy-related anxiety, and childhood adversity with class membership were investigated by regressing class membership on these factors in multinomial logistic regression analyses. First, each predictor was included one by one in separate regression models (see Table 4). Second, all significant predictors were included simultaneously in one multiple multinomial logistic regression analyses, to investigate their unique contributions. The minimum symptoms class was chosen as the reference class.
Predictors of Class Membership: Results From Multinomial Logistic Regression Models
In the univariate models, membership in the pregnancy-only class was predicted by several psychosocial factors, as fewer years of education, previous psychopathology, attachment-related anxiety and avoidance, pregnancy-related anxiety, and adverse childhood experiences all increased the odds of belonging to this class compared with the minimum symptoms class. For the postpartum-only class, only previous psychopathology showed a significant increase in odds ratios. Several psychosocial factors predicted class membership in the moderate-persistent class, including previous psychopathology, fewer years of education, increased scores on attachment-related anxiety and avoidance, stressful life events, pregnancy-related anxiety, and childhood adversities. Age and parity were unrelated to class membership.
In the multivariate model, only pregnancy-related anxiety remained a significant predictor of the pregnancy-only class. However, the odds ratio for previous psychopathology remained elevated (OR = 2.32, p = .09), although not significant, possibly indicating low statistical power. For the postpartum-only class results were similar to the univariate analysis, as only previous psychopathology significantly increased the odds of class membership as compared with the minimum symptoms class. Several predictors still distinguished the moderate-persistent class from the minimum symptoms class, as previous psychopathology, fewer years of education, as well as increases in partner-related anxiety and life stress showed significantly elevated odds ratios.
When comparing the three classes with elevated trajectories to one another by means of multinomial logistic regression analyses, some significant associations emerged (see supplementary Table 1). Individuals in the pregnancy-only class reported higher pregnancy-related anxiety relative to both the postpartum-only and the moderate increasing class in both univariate and multivariate analyses, even though the difference between the pregnancy-only and the moderate increasing class was only marginally significant in the multivariate analysis (p = .056). The most notable finding for the for the postpartum-only class was that it had significantly lower odds of both attachment anxiety and avoidance relative to the two other elevated trajectory classes in univariate analyses, as well as avoidance in multivariate analyses. The postpartum-only class thus resembled the minimum symptoms class with regard to partner-related attachment.
DiscussionIn this study, a growth mixture model of four distinct latent piecewise trajectory classes accounted for the heterogeneity of depressive symptom course among women during pregnancy and 12-months postpartum. The four classes were labeled according to trajectory characteristics as pregnancy-only (4.4%), postpartum-only (2.2%), moderate-persistent (10.5%), and minimum symptoms (82.9%). Referring back to our initial hypothesis about trajectory features, we found: (a) one trajectory with elevated symptoms limited to the pregnancy period; (b) one trajectory with stable low symptoms during pregnancy, rapidly increasing after birth with a gradual recovery the first postpartum year, termed postpartum-only; (c) a trajectory characterized with moderately elevated symptom levels during pregnancy, with a slight increase in symptom burden postpartum; (d) no class with a high chronic trajectory, contrary to our expectations; and (e) one trajectory including the majority of women without elevated depressive symptoms, as evident in the minimum symptoms class.
Thus, with the notable exception of not identifying a class characterized by persistent, severely elevated depressed mood, all expectations regarding class trajectories were met. Not finding a chronically elevated class with a high symptom burden has at least two possible explanations: Our sample did not include a sufficient number of participants with severe depressive symptoms. Alternatively, our statistical modeling choice of a three-piece piecewise model facilitated a close mapping of symptom change, whereas other statistical models may overestimate the stability of symptoms in women reporting high levels of depressive symptoms at several, but not all occasions.
Regarding our second aim, all psychosocial adversity factors as well as education distinguished the elevated trajectory classes from the minimum symptoms class. Further, in accordance with previous research distinguishing between remitting and chronic courses of PPD (Vliegen et al., 2014), the moderate-persistent class showed the highest number of associated psychosocial adversity factors. Overall, our findings were consistent with our hypothesis of heterogeneity in pathways of elevated depressive mood during pregnancy and the first postpartum year, connecting distinct trajectories of time courses with differential psychosocial adversity factors.
The pregnancy-only class consisted of women with an elevated initial level of depressive symptoms that rises steeply throughout pregnancy. After birth, however, symptoms are ameliorated and the women did not report further elevated depressed mood. Pregnancy-related anxiety seemed to be of particular importance for this class, as anxiety was the only adversity factor differentiating between the pregnancy-only class and all other classes, both in univariate and multivariate analyses. One potential explanation for this finding may be that depressive symptoms that are limited to pregnancy are a result of negative emotions and cognitions related to pregnancy and birth. This would fit the pattern of rapid symptom increase as the due date approaches followed by a quick amelioration of symptom burden after child delivery.
The second trajectory class follows a typical PPD-pattern (Wisner et al., 2010) comparable with Mora et al.’s (2009) early postpartum class and corresponds with studies of increased risk the first few weeks postpartum (Munk-Olsen et al., 2006). Surprisingly, the various measures associated with membership in the other trajectory classes did not increase odds of belonging to the postpartum-only class. Of the psychosocial factors measured in this study, only previous psychopathology increased the odds—by threefold. Further, higher partner-related attachment avoidance and anxiety decreased the odds of belonging to this group compared with the other two elevated trajectory classes. A tenable interpretation of this might be that this class represents a subgroup of women for whom the development of depressive symptoms is associated with factors not belonging to the psychosocial domain, or alternatively that there are other psychosocial antecedents not covered in this study.
The moderate-persistent class is characterized by a consistently elevated symptom level, with increasing symptoms as time passes after birth. This is in line with Patton et al. (2015) finding that for a large proportion of women with PPD, it represents a continuation of earlier mental health problems, as well as studies identifying symptom onset during pregnancy (Wisner et al., 2013). A noteworthy finding is that the mean estimate trajectory for this group is close to the threshold between subclinical and clinical levels, and—as within-class variation is allowed in the analyses—individual trajectories included in this group will be located both above and below the clinical cut-offs. This emphasizes the importance of subclinical variance, and of including dimensional approaches in this area of research. Several psychosocial adversity factors increased the odds of belonging to this group, as fewer years of education, previous psychopathology, anxious attachment orientation, and stress all increased the odds of following the moderate-persistent trajectory relative to the minimum symptoms class. Notably, stress further distinguished this class from the pregnancy-only class in multivariate analysis, in accordance with Vliegen et al. (2014) who found life stress to be one of the factors distinguishing a persistent course of PPD from a remitting course.
About 83% of the women, belonging to the minimum symptoms class, reported consistently low levels or no symptoms of depression throughout the period of study. This is in accordance with most prevalence reports (Biaggi et al., 2016; Gavin et al., 2005), although direct comparisons are difficult due to differences in assessment periods, methods, and populations (O’Hara & Wisner, 2014). The proportion of the minimum symptoms class in this study is also comparable with previous reports of heterogeneous time courses (Cents et al., 2013; Mora et al., 2009; Sutter-Dallay et al., 2012).
Limitations, Strengths, and Conclusions
There are important limitations of this study. First, the representativeness of the sample can be questioned. Figures from Statistics Norway (2014) indicate that our sample has a higher educational level than the general population. The response rate was 50.7%. As in any community-based research, there is a possibility of self-selection bias with an overrepresentation of healthy and resourceful participants; in this particular study there is a threat of underrepresentation of women with heightened levels of depressive symptoms as they might find participation in research too demanding. This might limit the generalizability of results.
A related concern is selective dropout, and analyses indeed showed that some demographic and psychosocial factors, including depressive symptoms, predicted attrition. However, by using contemporary missing data routines, including FIML and models not assuming MAR, we attempted to reduce the impact of such selective attrition. Yet another concern regarding representativeness is the specific cultural context. There is evidence of considerable variation of PPD prevalence rates across nations and cultures (Halbreich & Karkun, 2006), and it is possible that the relatively generous social welfare policies in Norway (i.e., free prenatal care, a year of paid parental leave) might have a preventive effect on PPD symptoms, thus potentially limiting generalizability in countries with less generous welfare policies. On the other hand, like many Western societies individualistic values are emphasized in Norway, whereas other societies may provide protective factors in endorsing cultural patterns that reinforce the maternal role and effectively relieve new mothers of burdens (Halbreich & Karkun, 2006).
Second, particularly the pregnancy-only and the postpartum-only classes were small in size, including n = 41 and n = 20 participants, respectively. Consequently, our study is limited by the resulting low statistical power to detect differences between these and other classes in multinomial logistic regression analyses. For example, the nonsignificant finding despite relatively high odds ratios regarding previous psychopathology in the pregnancy-only class may be due to low power.
Third, previous psychopathology was assessed by means of self-reports, based on a single item, including all kinds of psychopathology. The severity, timing, and nature of earlier mental health problems thus remain unknown. Ideally, one ought to have objective measures of the participants’ histories of affective disorders. Relatedly, depressive symptoms were measured by self-report only, and therefore provide no information about a clinical diagnosis of depression.
Fourth, stress exposure was measured by a self-reported life event checklist. Although there are reports of satisfying reliability and validity of the instrument we have used (Abidin, 1995), in general this assessment method has received criticism for having methodological limitations such as assuming that the life events listed have the same meaning across contexts and individuals (Harkness & Monroe, 2016). Some caution in the interpretation of stress is therefore warranted.
Fifth, we only included predictors at enrollment, and did not investigate the potential influence of time-varying covariates such as treatment received, birth complications, and infant health.
Despite these limitations, the present study has identified four trajectory classes of depressive symptom course in the pregnancy and postpartum period, as well as predicted class membership on the basis of psychosocial factors. Findings suggest that pregnancy and postpartum depressive symptom onset and development do not follow a uniform course, nor are predicted by a singular set of factors, but rather support a model of differential time courses associated with diverse psychosocial adversities.
Researchers and clinicians should be aware of possible heterogeneous symptom development trajectories, and subsequently inquire about diverse underlying mechanisms, pathogenic pathways and prognosis, in order to refine theories and develop targeted prevention and intervention. Future research is needed to test differential diathesis-stress models for trajectory classes. An important next step would be to investigate the differential outcomes of trajectory classes on maternal health, child development, and family functioning.
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Submitted: May 6, 2016 Revised: November 4, 2016 Accepted: November 4, 2016
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Source: Journal of Abnormal Psychology. Vol. 126. (2), Feb, 2017 pp. 173-183)
Accession Number: 2016-58118-001
Digital Object Identifier: 10.1037/abn0000246
Record: 39- Title:
- Posttraumatic stress in deployed Marines: Prospective trajectories of early adaptation.
- Authors:
- Nash, William P.. Boston VA Research Institute, MA, US
Boasso, Alyssa M.. VA Boston Healthcare System, Jamaica Plain, MA, US
Steenkamp, Maria M.. VA Boston Healthcare System, Jamaica Plain, MA, US
Larson, Jonathan L.. VA Boston Healthcare System, Jamaica Plain, MA, US
Lubin, Rebecca E.. VA Boston Healthcare System, Jamaica Plain, MA, US
Litz, Brett T.. VA Boston Healthcare System, Jamaica Plain, MA, US, brett.litz@va.gov - Address:
- Litz, Brett T., VA Boston Healthcare System, 150 South Huntington Avenue, 13- B74, Jamaica Plain, MA, US, 02130, brett.litz@va.gov
- Source:
- Journal of Abnormal Psychology, Vol 124(1), Feb, 2015. pp. 155-171.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 17
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- military, trajectory, PTSD, dissociation, coping
- Abstract:
- We examined the course of PTSD symptoms in a cohort of U.S. Marines (N = 867) recruited for the Marine Resiliency Study (MRS) from a single infantry battalion that deployed as a unit for 7 months to Afghanistan during the peak of conflict there. Data were collected via structured interviews and self-report questionnaires 1 month prior to deployment and again at 1, 5, and 8 months postdeployment. Second-order growth mixture modeling was used to disaggregate symptom trajectories; multinomial logistic regression and relative weights analysis were used to assess the role of combat exposure, prior life span trauma, social support, peritraumatic dissociation, and avoidant coping as predictors of trajectory membership. Three trajectories best fit the data: a low-stable symptom course (79%), a new-onset PTSD symptoms course (13%), and a preexisting PTSD symptoms course (8%). Comparison in a separate MRS cohort with lower levels of combat exposure yielded similar results, except for the absence of a new-onset trajectory. In the main cohort, the modal trajectory was a low-stable symptoms course that included a small but clinically meaningful increase in symptoms from predeployment to 1 month postdeployment. We found no trajectory of recovery from more severe symptoms in either cohort, suggesting that the relative change in symptoms from predeployment to 1 month postdeployment might provide the best indicator of first-year course. The best predictors of trajectory membership were peritraumatic dissociation and avoidant coping, suggesting that changes in cognition, perception, and behavior following trauma might be particularly useful indicators of first-year outcomes. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Disease Course; *Military Deployment; *Posttraumatic Stress Disorder; Avoidance; Coping Behavior; Dissociative Disorders; Social Support; Symptoms; Trauma
- PsycINFO Classification:
- Neuroses & Anxiety Disorders (3215)
Military Psychology (3800) - Population:
- Human
Male - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Posttraumatic Stress Disorder Checklist
World Health Organization Disability Assessment Scale-II
Life Events Checklist
General Post-Deployment Support Scale
Brief COPE
Beck Depression Inventory–II DOI: 10.1037/t00742-000
Childhood Trauma Questionnaire DOI: 10.1037/t02080-000
Clinician-Administered PTSD Scale DOI: 10.1037/t00072-000
Deployment Risk and Resilience Inventory DOI: 10.1037/t04522-000
Peritraumatic Dissociative Experiences Questionnaire DOI: 10.1037/t07470-000 - Grant Sponsorship:
- Sponsor: VA Health Service Research and Development, US
Grant Number: SDR 09-0128
Recipients: No recipient indicated
Sponsor: U. S. Marine Corps, US
Recipients: No recipient indicated
Sponsor: Navy Bureau of Medicine and Surgery, US
Recipients: No recipient indicated - Methodology:
- Empirical Study; Longitudinal Study; Quantitative Study
- Supplemental Data:
- Tables and Figures Internet
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Nov 24, 2014; Accepted: Oct 6, 2014; Revised: Oct 5, 2014; First Submitted: Feb 8, 2014
- Release Date:
- 20141124
- Correction Date:
- 20150216
- Copyright:
- American Psychological Association. 2014
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/abn0000020; http://dx.doi.org.offcampus.lib.washington.edu/10.1037/abn0000020.supp(Supplemental)
- PMID:
- 25419860
- Accession Number:
- 2014-49228-001
- Number of Citations in Source:
- 90
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-49228-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-49228-001&site=ehost-live">Posttraumatic stress in deployed Marines: Prospective trajectories of early adaptation.</A>
- Database:
- PsycINFO
Posttraumatic Stress in Deployed Marines: Prospective Trajectories of Early Adaptation
By: William P. Nash
Boston VA Research Institute
Alyssa M. Boasso
VA Boston Healthcare System and Massachusetts Veterans Epidemiology Research and Information Center
Maria M. Steenkamp
VA Boston Healthcare System, Massachusetts Veterans Epidemiology Research and Information Center, and Boston University School of Medicine
Jonathan L. Larson
VA Boston Healthcare System and Massachusetts Veterans Epidemiology Research and Information Center
Rebecca E. Lubin
VA Boston Healthcare System and Massachusetts Veterans Epidemiology Research and Information Center
Brett T. Litz
VA Boston Healthcare System, Massachusetts Veterans Epidemiology Research and Information Center, and Boston University School of Medicine;
Acknowledgement: This study was funded by VA Health Service Research and Development (SDR 09-0128) and by the U. S. Marine Corps and Navy Bureau of Medicine and Surgery. The authors acknowledge the Marine Resiliency Study (MRS) team, General John M. Paxton Jr., USMC, and Debbie Paxton, RN, who made this work possible. We also thank Kevin Grimm, who provided statistical feedback and suggestions.
Posttraumatic stress disorder (PTSD) is a psychopathological condition for which the course of symptoms, as they evolve over time, is of particular theoretical and practical importance. It is one of a small group of mental disorders, termed trauma- and stressor-related disorders in the fifth edition of the Diagnostic and the Statistical Manual of Mental Disorders (DSM-5; American Psychiatric Association, 2013), whose onset is temporally linked to a specific triggering event. To be diagnosed, symptoms must persist beyond the first 30 days following trauma exposure, and one of the two subtypes of PTSD, delayed PTSD, is characterized by symptoms that first appear 6 months after the event. Moreover, the time course of PTSD symptoms is central to the study of adaptation to trauma, and key concepts such as risk, resilience, and recovery have clear meanings only when defined in terms of changes in symptoms and functioning over time (Layne, Warren, Watson, & Shalev, 2007). Tracking the course of PTSD symptoms over time might identify the processes that determine long-term outcomes, discriminating between normative and pathological responses to experiences of trauma and recognizing the points at which early interventions might positively influence outcomes.
The theoretical and practical implications of the time course of PTSD symptoms might be especially salient for the military, which bears primary responsibility for preventing negative psychological health outcomes such as PTSD in service members whose occupations place them at high risk for exposure to potentially traumatic events. For military prevention and early intervention programs, one key question can only be answered in terms of the time course of posttraumatic stress symptoms: among service members exposed to potentially traumatic events, at which point in their symptom courses can those at greatest risk for developing chronic PTSD be identified so that their further exposure to potentially traumatic events can be limited and early interventions to promote recovery can be provided?
Early cross-sectional and retrospective research on PTSD in both military and nonmilitary populations divided groups of people who had been exposed to potentially traumatic events into those who exceeded diagnostic thresholds for the disorder and those who did not, ignoring potential heterogeneity within those two categories. More recent longitudinal studies in many different populations suggest that posttraumatic stress symptoms might trace a number of distinct trajectories over time, which can be grouped into clusters of approximately similar patterns (Bonanno, 2004). The research question addressed by these studies is whether between-individual differences in adaptation to exposure to potentially traumatizing events generate groups of people following similar within-individual courses. A number of statistical tools can be used to answer this question, including growth models, growth mixture models, and latent profile analysis; each procedure has strengths and weaknesses. PTSD researchers have chosen to use growth mixture modeling (GMM), a data-driven statistical procedure that groups participants on the basis of their intra-individual change patterns (e.g., McArdle & Epstein, 1987; Muthén, 2004). In this article, we report the results of GMM analyses of posttraumatic stress trajectories in two cohorts of U.S. Marines enrolled in the Marine Resiliency Study (MRS; see Baker et al., 2012), assessed approximately 1 month prior to their 7-month deployments to Afghanistan, then reexamined at three time points during the 8 months immediately following their return from deployment. We chose GMM chiefly to maximize the comparability of the results with prior studies of deployed service members, all of which employed GMM.
To date, four longitudinal studies have used GMM to identify latent PTSD trajectories in military populations: U.S. Army soldiers followed for 9 months after they deployed to Kosovo on a 6-month NATO-led peacekeeping mission in 2002 (Dickstein et al., 2010); U.S. Army soldiers assessed within 5 days of their return from the Gulf War in 1991 and again at 1.5 and 6 years later (Orcutt, Erickson, & Wolfe, 2004); Danish soldiers followed for 7 months after their return from a 6-month deployment to Afghanistan in 2009 (Berntsen et al., 2012); and a large and heterogeneous cohort of military service members who deployed one or more times to Iraq or Afghanistan between 2001 and 2008 (Bonanno et al., 2012). All of these studies found evidence for qualitatively distinct trajectories of PTSD symptoms, the exact nature of which depended on the sample, methodology, and context. Trajectories characterized by low symptom levels at all time points were modal in all four studies, comprising between 57% and 84% of each sample. Chronic symptoms or new-onset symptoms were uncommon, each representing less than 10% of the sample, when present. Currently, there are no studies of the course of PTSD symptoms in a sample of U.S. service members from a single military unit deployed together for combat duties in Iraq or Afghanistan. Bonanno et al.’s (2012) sample was more heterogeneous, comprising members of all service branches and all occupational fields, many of whom did not serve in combat roles. No existing studies have reported PTSD symptom trajectories in a cohort of U.S. service members deployed to a war zone specifically to engage in ground combat, a population at high risk for combat-related PTSD and, therefore, of great interest to leaders of military PTSD prevention, screening, and treatment programs.
In this study, we chose five sets of self-reported predictor variables relevant to the military that have been repeatedly found to correlate with posttraumatic stress outcomes: combat-related stressor exposures experienced during deployment, lifetime stressor exposures experienced outside the index deployment, perceived social support during and after deployment, peritraumatic dissociation, and avoidant coping. Combat exposure has consistently been shown to be a leading risk factor for PTSD in military personnel, typically in a dose-response fashion (e.g., Dohrenwend et al., 2006; Foy, Sipprelle, Rueger, & Carroll, 1984; Green, Grace, Lindy, Gleser, & Leonard, 1990; King, King, Foy, Keane, & Fairbank, 1999). As predicted by diathesis–stress models of PTSD (e.g., McKeever & Huff, 2003), greater prior life span trauma exposure has been found to confer heightened risk for combat-related PTSD, and high rates of predeployment trauma are present in military personnel (e.g., Clancy et al., 2006; Vogt, Pless, King, & King, 2005; Vogt et al., 2011; Zaidi & Foy, 1994). Because we are interested in the relationship between these two variables, we examined the influence of the interaction between prior trauma and combat exposure on PTSD symptom course. Consistent with the diathesis–stress model, we expected prior life span trauma to moderate the relationship between combat exposure and new-onset or chronic PTSD symptom trajectories. We expected social support during and after deployment to buffer adverse psychological outcomes because it mitigates distress and promotes shared meaning making (e.g., Brailey, Vasterling, Proctor, Constans, & Friedman, 2007), and veterans with PTSD consistently report lower unit support and postdeployment social support (e.g., Keane, Scott, Chavoya, Lamparski, & Fairbank, 1985; Pietrzak et al., 2010).
Peritraumatic dissociation, which entails transient alterations in the normal integration of cognitive, emotional, somatic, and behavioral processes during or immediately after a potentially traumatic event, was included as a predictor variable because it is a marker for stressors experienced by a given person at a given point in time that exceeded their current adaptive capacity, as predicted by the stress injury model of PTSD (Nash, 2007; Nash, Silva, & Litz, 2009; Nash et al., 2010). The stress injury model does not predict which variables confer risk or are protective per se, but focuses on the relationship between moment-to-moment stress levels and ideographic stress breaking points determined by fluctuating biological, psychological, and social functional capacities. According to this model, stress outcomes that follow superthreshold stressor exposures, including subclinical stress injuries and mental disorders such as PTSD, are more likely to be pathological than outcomes that follow less extreme stressor experiences, which are more likely to be normative. Previous studies have found peritraumatic dissociation to be associated with more adverse postevent outcomes, and trauma-exposed people with PTSD are more likely to report having experienced peritraumatic dissociation than did those without PTSD (e.g., Bremner & Brett, 1997; Marmar et al., 1994; O’Toole, Marshall, Schureck, & Dobson, 1999; Tichenor, Marmar, Weiss, Metzler, & Ronfeldt, 1996). Avoidant coping, characterized as utilizing distraction, denial, or disengagement as mechanisms to manage problems, is hypothesized to increase risk for PTSD in two ways: habitual avoidant coping leading up to exposure to a traumatic stressor might contribute to vulnerability for PTSD, and, in the aftermath of trauma, overgeneralized avoidant coping and self-soothing repertoires might lessen the likelihood of corrective recovery-promoting experiences. Avoidant coping has repeatedly been found to correlate with PTSD symptom severity (e.g., Pietrzak, Harpaz-Rotem, & Southwick, 2011; Sharkansky et al., 2000; Solomon, Mikulincer, & Benbenishty, 1989). Service members with PTSD have also been shown to be more likely to use avoidant rather than nonavoidant coping strategies (e.g., Sutker, Davis, Uddo, & Ditta, 1995), whereas decreased use of avoidant coping over time has been associated with recovery from combat stress (Solomon, Mikulincer, & Avitzur, 1988).
On the basis of previous longitudinal studies of PTSD symptom courses, we predicted that six trajectories of PTSD symptom severity would best describe the data: (1) a quadratic recovery course characterized by low predeployment symptoms, followed by high initial postdeployment symptoms, and then a marked decrease in symptoms toward baseline; (2) a relatively flat low–stable course with low symptom levels across all time points; (3) a new- onset course characterized by high and relatively unremitting symptoms across all postdeployment time points that follow a low level of PTSD symptoms prior to deployment; (4) a preexisting–improving course characterized by high levels of PTSD prior to deployment followed by a decrease in symptom levels postdeployment; (5) a preexisting–chronic course characterized by high levels of PTSD prior to deployment that do not decrease during the postdeployment period; and (6) a delayed course characterized by low symptom levels before and immediately after deployment, but an increase in PTSD-symptom burden during the 8 months following return from deployment. Given the high frequency of significant warzone stressors expected in Afghanistan and the high levels of resilience expected in highly trained Marines, we predicted that the recovery trajectory would be most prevalent. We expected the next most prevalent courses to be low–stable and new-onset courses. Given prior warzone deployments and other predeployment stressor exposures in our cohort, we expected the two preexisting PTSD courses to also occur in significant percentages of participants.
We predicted that combat-related experiences during the index deployment would best predict membership in the new-onset, delayed, and preexisting–chronic trajectories and that prior life span stressor exposures would best predict membership in the preexisting–improving and preexisting–chronic trajectories. We expected prior life span trauma and combat experiences during the index deployment to interact to increase vulnerability for the new-onset, chronic, and delayed PTSD courses. That is, we predicted that Marines with extensive trauma histories would be affected by relatively lower doses of combat exposure, tracing worse PTSD outcomes over time than Marines with similar levels of combat exposure but no prior trauma. We expected peritraumatic dissociation and avoidant coping to confer risk for all persistently negative outcomes, including the new-onset, delayed, and preexisting–chronic trajectories. Conversely, we expected perceived social support to serve a protective function, with Marines in the recovery, low–stable, and preexisting–improving trajectories reporting greater perceived social support than Marines in the new-onset, delayed, and preexisting–chronic trajectories. Assessment of the relative importance of these predictors, using relative weights analysis, was largely exploratory.
Method Design and Participants
The data source for this study was the MRS, a longitudinal field study of four consecutive all-male cohorts (named Cohorts 1, 2, 3, and 4) of active-duty ground-combat Marines, each recruited primarily from a single infantry battalion scheduled to deploy to Iraq or Afghanistan between 2008 and 2011 from either Marine Corps Base Camp Pendleton or Marine Corps Air Ground Combat Center, Twenty-Nine Palms, both in California (Baker et al., 2012). Four assessment time points were planned for each cohort: 1 month prior to its 7-month deployment, and 1 week, 3 months, and 6 months postdeployment. Overall, 2,593 Marines completed the Time-0 (T0) predeployment assessment; 2,317 (89.3%) completed the Time-1 (T1) assessment; 1,901 (73.3%) completed the Time-2 (T2) assessment; and 1,634 (63.0%) completed the Time-3 (T3) assessment. Participation at each assessment was voluntary and individual informed consent was obtained before enrollment at baseline with no senior unit leaders present.
For this study, full analyses focused exclusively on Cohort 4, whereas Cohort 3 was used for a post hoc comparison of latent trajectory patterns. Cohorts 1 and 2 were excluded because their PTSD scores at baseline were indexed exclusively to military events, whereas their postdeployment PTSD scores were indexed to any currently distressing lifetime events; this threat to internal validity made an examination of PTSD symptom trajectories problematic across time in Cohorts 1 and 2. In Cohorts 3 and 4, PTSD symptoms were indexed at all time points to any currently distressing lifetime event. Cohorts 3 and 4 were analyzed separately to avoid two other internal validity problems. The first of these arose because modal postdeployment assessment times differed by as much as 3 months between these cohorts. The second reason we analyzed Cohorts 3 and 4 separately was because these cohorts predominantly comprised members of two distinct Marine infantry battalions that trained, deployed, and then returned as units from two very different sets of warzone challenges. Combining them into a single larger sample might introduce a number of uncontrolled between-unit variances. Cohort 4 deployed to Helmand Province in Afghanistan in late 2010, when U.S. forces sustained their highest causality rates. Cohort 3, having deployed earlier than did Cohort 4, before the heaviest fighting began, reported significantly lower combat exposure than Cohort 4, t(1,926) = 14.27, p < .001. For this study, Cohort 4 (N = 892) offered the best opportunity to examine the course of PTSD in highly combat-exposed U.S. service members. We used Cohort 3 (N = 673) to compare the GMM results of Cohort 4 with a sample of similarly assessed Marines with less overall combat exposure.
For this study, we removed Marines who did not deploy (9 in Cohort 3 and 4 in Cohort 4) and those who died during deployment (2 in Cohort 3 and 17 in Cohort 4). To address variability around the modal postdeployment assessment times, which differed between Cohorts 3 and 4, we used the following procedures, outlined by King et al. (2006). For the three postdeployment assessments, scores on all measures were assigned to three follow-up date ranges determined by the count of days since the date of return from deployment. We aimed to minimize the dispersion of days within each date range and to maximize the number of included participants. This was done separately for Cohorts 3 and 4. The date ranges across Cohorts 3 and 4 differed only with respect to the second postdeployment assessment (T2). The ranges of days that best fit the data were 20 to 40 days for T1 (Cohort 3: M = 30, SD = 6; Cohort 4: M = 30, SD = 4), 80 to 100 days for T2 for Cohort 3 (M = 84, SD = 3), but 140 to 160 days for T2 for Cohort 4 (M = 153, SD = 4), and 240 to 260 days for T3 (Cohort 3: M = 251, SD = 2; Cohort 4: M = 249, SD = 5). In other words, on average, assessments for Cohort 4 occurred 1 month predeployment (T0) and 1-month (T1), 5-months (T2), and 8-months (T3) postdeployment. For Cohort 3, on average, assessments occurred 1 month predeployment (T0) and 1-month (T1), 2-months (T2), and 8-months (T3) postdeployment. Once the data were redistributed according to actual date ranges, 4 Marines in Cohort 4 and 4 Marines in Cohort 3 were missing data at all time points and were excluded from analyses. For Cohort 4, the final subsample of responders consisted of 867 Marines: 859 at baseline, 554 at T1, 328 at T2, and 287 at T3. For Cohort 3, the final subsample of responders consisted of 658 Marines: 653 at baseline, 377 at T1, 382 at T2, and 215 at T3.
Table 1 displays statistical comparisons at baseline (T0) between Marines whose PTSD symptom severity data were available at each subsequent time point (responders) and Marines missing PTSD outcome data at those time points (nonresponders). Nonresponders at T1, T2, and T3 were more likely to have previously deployed. In addition, nonresponders at T2 were more functionally impaired and had more prior lifetime trauma. Nonresponders at T3 were more educated, and nonresponders at T2 and T3 were older. In the final Cohort 4 sample, Marines were primarily Caucasian (83.1%). At baseline, participants had served an average of 3.10 (SD = 3.15) years in the military and 51.54% had deployed at least once before. Participants’ ages at baseline ranged from 18 to 43 (M = 23.16, SD = 3.67); 68.1% had no more than a high school diploma, and 41.1% were married (see Table 2). Sample information for Cohort 3 can be found in Tables 1 and 2 of the supplemental materials.
Responder and Nonresponder Comparisons on Variables Reported at Time 0 (T0)
Risk, Resilience, and Mental Health Factors Throughout the Deployment Cycle
Outcome Measures
PTSD
PTSD symptom severity was assessed using both a structured clinical interview, the Clinician Administered PTSD Scale (CAPS; Blake et al., 1995), and a self-report questionnaire, the Posttraumatic Stress Disorder Checklist (PCL; Weathers, Litz, Herman, Huska, & Keane, 1993). The CAPS was used at T0, T2, and T3, but was not used at T1 to minimize participant burden in the early weeks postdeployment. The PCL was used at all time points. At every time point, CAPS and PCL assessments were indexed to any lifetime traumatic event endorsed by the participant as currently most distressing. Consequently, index events were allowed to change during the course of the study, ensuring the capture of maximum symptom burden at each time point.
The CAPS assesses the frequency and intensity of PTSD symptoms, each rated on a Likert-type scale. ranging from 0 (“Never” or “None”) to 4 (“Daily or almost daily” or “Extreme, incapacitating distress, cannot dismiss memories, unable to continue activities”). Total CAPS PTSD symptom severity was calculated by summing the frequency and intensity scales for each item (yielding a range of 0 to 136; Blake et al., 1995). Raters were systematically trained and certified doctoral-level personnel. All CAPS interviews were audio recorded and 15% were randomly selected and co-rated to determine interrater reliability (intraclass correlation coefficient [ICC] = .99; Shrout & Fleiss, 1979). The PCL assesses the severity of PTSD symptoms on a 1 (not at all bothersome) to 5 (extremely bothersome) Likert-type scale. Total PTSD symptom burden was calculated by summing across all 17 symptoms (yielding a range of 17 to 85). The CAPS and the PCL have been shown to have excellent psychometric properties in numerous studies with varied populations (see Weathers et al., 2001).
Convergent outcome indicators
To substantiate the class solutions generated by the GMM, we compared membership in the PTSD trajectories we found with four classes of outcomes we believed would covary with PTSD trajectory: full or subthreshold PTSD caseness based on diagnostic criteria, depression, anxiety, and overall functioning. Using the CAPS at T0, T2, and T3, we defined a PTSD diagnosis as meeting the minimum type and number of symptoms required by DSM–IV criteria (American Psychiatric Association, 2000), each rated at least at a frequency of 1 and a severity of 2 (Weathers et al., 1999). We defined subthreshold PTSD conservatively, by requiring a participant to meet the DSM–IV criteria for Category B symptoms and either Category C or D symptoms (e.g., Blanchard et al., 1995). Because the CAPS was not administered at T1, the PCL was used to determine full and subthreshold PTSD caseness at the initial postdeployment time point; a required symptom was considered present if it was endorsed on the PCL at a severity level of moderately (a value of 3) or above. We assessed depression using the Beck Depression Inventory-II (BDI-II; Beck, Steer, & Brown, 1996), a 21-item questionnaire that assesses symptoms of depression. The internal consistency of the BDI-II in our study was uniformly high (T0: α = .90; T1: α = .89; T2: α = .91; T3: α = .90). We assessed anxiety using the Beck Anxiety Inventory (BAI; Beck, Epstein, Brown, & Steer, 1988), which was also found to have high levels of internal consistency in the MRS (T0: α = .90; T1: α = .92; T2: α = .92; T3: α = .94). Summary scores for both scales at each time point were created by summing across all 21 items. We assessed overall functioning and self-reported disability using the 17-item World Health Organization Disability Assessment Scale-II (WHODAS; Smith & Epping-Jordan, 2000). Summary scores were calculated at each time point by summing across 12 core items that were endorsed on a five-point Likert-type scale, ranging from “None” to “Extreme/cannot do”. The internal consistency of the WHODAS was very high in the MRS (T0: α = .90; T1: α = .92; T2: α = .90; T3: α = .93). The reliabilities for the BDI-II, BAI, and WHODAS for Cohort 3 are shown in Table 3 of supplemental materials.
Fit Statistics for Class Solutions
Predictor Variables
Life span trauma
We assessed a history of previous highly stressful, potentially traumatic experiences using two measures at T0 only: the Childhood Trauma Questionnaire (CTQ; Bernstein et al., 1994) and the Life Events Checklist (LEC; Gray, Litz, Hsu, & Lombardo, 2004). The CTQ is a 28-item questionnaire that assesses the frequency of experiences of abuse or neglect during childhood, each endorsed on a five-point Likert-type scale, ranging from 1 (never true) to 5 (very often true). Childhood trauma summary scores are normally created using the CTQ by summing across its 25 items, grouped into five subscales of five items each. The MRS employed a modified 22-item version of the CTQ, with one item intentionally missing from each of the emotional abuse, physical abuse, and physical neglect subscales, as recommended by our institutional review board. To make modified 22-item CTQ summary scores comparable to childhood trauma scores calculated using all 25 items, we weighted all four-item subscales as if they also reflected responses to five items, then summed all subscale scores to create a composite. This total score was then divided by 25 to obtain a mean childhood trauma score (α = .92).
The LEC assesses lifetime exposure to 16 specific classes of highly stressful, potentially traumatic events. Response options are: happened to me, witnessed it, learned about it, not sure, and doesn’t apply. A prior lifetime trauma composite score was created by first assigning a “1” to each item endorsed as happened to me or witnessed it and a “0” to all other responses and then summing across the 16 events.
Warzone stressor exposures
We used two measures of warzone stressor exposure taken from the Deployment Risk and Resilience Inventory (DRRI; King, King, Vogt, Knight, & Samper, 2006), a collection of questionnaires assessing military deployment-related experiences. To assess exposure to combat events, we used a modified version of the Combat Experiences scale, a 15-item yes/no scale that assesses individual- or unit-level exposure to warzone-related stressors such as “I fired my weapon at the enemy.” The MRS modified the DRRI Combat Experiences scale by changing response choices to a Likert-type scale based on frequency of exposure, ranging from 0 (never) to 4 (daily or almost daily), as suggested by Vogt, Proctor, King, King, and Vasterling (2008); it changed item wording to restrict focus to the personal experiences of the respondent; and added an additional item assessing participation in logistical support convoys. A combat exposure composite was created by averaging across all items (α = .91). We assessed perceptions of danger during deployment using the Perceived Threat scale, a 15-item questionnaire that assesses fear for personal safety and well-being in the warzone, with Likert-type response choices, ranging from 1 (strongly disagree) to 5 (strongly agree). A perceived threat composite was calculated as the mean of all 15 items (α = .84). The two DRRI scales were administered at T1 only.
Peritraumatic dissociation
The Peritraumatic Dissociative Experiences Questionnaire (PDEQ; Marmar, Weiss, & Metzler, 1997) is a 10-item measure of dissociative experiences that uses Likert-type scale response choices that range from 1 (not true at all) to 5 (extremely true). The MRS version asked participants to report dissociative experiences that occurred during the worst event from their most recent deployment, assessed only at T1 (α = .88). An index of peritraumatic dissociation was calculated as the mean of all 10 items.
Social support
We used two questionnaires from the DRRI to measure perceived social support: The Unit Support scale and the General Post-Deployment Support scale. The Unit Support scale includes 12 items that assess perceived levels of cohesion and camaraderie within the military unit during deployment, each rated on a five-point Likert-type scale, ranging from 1 (strongly disagree) to 5 (strongly agree). We calculated unit support composite scores by averaging across all items at T1 (α = .93). The General Post-Deployment Support scale uses 15 items to assess perceptions of social support from all sources, including family, friends, and community, since returning from deployment, each rated on a five-point Likert-type scale, ranging from 1 (strongly disagree) to 5 (strongly agree). We created a composite of general social support by averaging all 15 items across all three postdeployment time points, T1–T3 (T1: α = .87; T2: α = .89; T3: α = .89).
Avoidant coping
We assessed avoidant coping using subscales from the Brief COPE, a 28-item questionnaire that assesses 14 different coping styles, each rated on a four-point Likert-type scale, ranging from 1 (I have not been doing this at all) to 4 (I have been doing this a lot; Carver, 1997). The Brief COPE does not specify a time frame in which coping strategies should be self-assessed and reported but asks to what extent each identified coping strategy seems to have been habitual at each assessment time point. To create an index of avoidant coping across the deployment cycle, we averaged scores on five 2-item subscales of the Brief COPE (Schnider, Elhai, & Gray, 2007) across all four data-collection time points: self-distraction, denial, behavioral disengagement, self-blame, and substance use (T0: α = .81; T1: α = .80; T2: α = .81; T3: α = .82).
Data Analysis Plan
It is worth underscoring limitations to the use of mixture models (the underlying statistical framework for GMMs) and the approaches we took to minimize their impact. The first limitation is that the latent classes in mixture models are not necessarily real entities. As noted, the mixture modeling approach to classification is data-driven: The model attempts to maximize a likelihood function that involves mixture distributions, with each component of the mixture distribution referred to as a latent class, even though these derived groups might not be entirely distinct. To minimize the challenges of using mixture modeling, we examined the interpretability of the latent classes we found and attempted to replicate the pattern of latent classes we found in a second sample. The second limitation is that mixture models are sensitive to starting values. To minimize the impact of this sensitivity, we fit the mixture models with multiple random sets of starting values to ensure that the global maximum was reached. Additionally, the same set of parameter estimates were obtained from multiple sets of starting values suggesting that the solutions were stable. The third limitation involves how the relative fitness of alternative models is compared. Mixture models with a different number of classes are not statistically nested. Thus, researchers are limited to using information criteria (e.g., Bayesian information criterion, Akaike information criterion) and a variety of additional comparative fit indices (e.g., bootstrap likelihood ratio test, Lo-Mendell-Rubin likelihood ratio Test), which often converge to different recommendations. Our approach to model comparison involved a thorough examination of all fit indices and weighed substantive interpretation and classification quality (e.g., entropy). The fourth limitation is the reliance of the mixture model on non-normality. Mixture models attempt to account for non-normal distributions with mixture distributions; however, non-normality might be due to a variety of issues (e.g., poor sampling, poor measurement, etc.; see Bauer & Curran, 2003; Grimm & Ram, 2009). To address this limitation, we thoroughly examined the interpretability of the latent classes, attempted to replicate the solution in a second sample, and fit second-order models to limit the impact of poor measurement. Additionally, we ensured that our chosen model contained latent classes that were distinguishable in terms of initial status, change trajectories, and predictor variables and outcomes (Erosheva, Matsueda, & Telesca, 2014).
Second-Order Growth Mixture Model
GMM is a person-centered analysis that identifies subgroups within a given sample that are defined by a common pattern of change in an outcome variable over time (Jung & Wickrama, 2008). We used a second-order growth mixture model (SOGMM) that combines GMM with factor models comprising multiple measurements of the outcome. SOGMM, compared with GMM, produce outcomes that better control for measurement error and limit the likelihood that an inappropriate class solution will be identified (Bauer & Curran, 2003; Grimm & Ram, 2009). This analysis strategy capitalized on the multiple assessments of PTSD (CAPS and PCL) across all time points and allowed us to create a latent PTSD variable across all time points, obviating the problem of not having CAPS data at T1. Keeping data from all four assessment time points allowed us to examine quadratic effects.
Under the assumption of ignorable missingness (Schlomer, Bauman, & Card, 2010), using Mplus (Version 7.1) we employed full information maximum likelihood (FIML) estimation for all procedures leading up to and including the unconditional SOGMM. To configure the SOGMM, we first created a longitudinal factor model with latent variables representing PTSD for each time point (the first-order model). Each PTSD latent variable comprised a CAPS and a PCL severity score, except for T1 when the CAPS was not administered. The missing CAPS data point was accommodated by creating a missing variable placeholder within the factor model (e.g., King et al., 2009; McArdle & Woodcock, 1997). Following recommendations for fitting multiple indicator models by Muthén and Muthén (2010) and Grimm and Ram (2009), we imposed strict measurement invariance across time (invariant loadings, residuals, and measurement intercepts) and conducted a confirmatory factor analysis (CFA). Next, we applied the GMM to the longitudinal factor model (see Figure 1). To identify the SOGMM the intercepts of the CAPS were set to 0. Within-class variance of the intercept and growth factors were freely estimated, whereas between-class variances were held equal. On the basis of previous studies, we estimated both linear and quadratic terms for the unconditional SOGMM assuming between one and six classes would best fit the data. Model estimation was an iterative process wherein modifications were made to account for estimation difficulties. Specifically, to correct for negative variances that were not significantly different from 0, the variances of the latent slope were set to 0 for the one-, three-, and six-class solutions; the variances of the quadratic variable were set to 0 for all except the four-class solution; the residual variances for the PCL were set to 0 for Class Solutions 4 and 6; and the residual variances of the latent T0 PTSD variable were set to 0 for Class Solutions 4 through 6.
Figure 1. Path diagram for the second-order growth mixture model (SOGMM). The model was constrained as follows: residual variances for the Clinician-Administered PTSD Scale (CAPS) and PTSD Checklist (PCL) were set equal across time, as were intercepts for the PCL, and factor loadings for the PCL on the latent posttraumatic stress disorder (PTSD) variable, intercepts for the CAPS were constrained to 0 for each time point, and factor loadings for the latent PTSD variables on the latent growth factors were set to 0 for the intercept.
Once we obtained properly estimated models, the best-fitting model was selected on the basis of prior research, class size (at least 5% of the total sample in the smallest class), parsimony, interpretability, formal fit indices, and classification quality. Models were considered to have a better fit and more accurate class assignment when they had a lower Bayesian information criterion (BIC), a lower sample size adjusted BIC (SSA-BIC), a significant Lo-Mendell-Rubin likelihood ratio test (LMR-LRT), a significant bootstrapped likelihood ratio test (BLRT), and higher entropy and average posterior probabilities (indices of classification certainty; for index accuracy see Nylund, Asparouhov, & Muthén, 2007; Jung & Wickrama, 2008). For Cohort 3, these same procedures for selecting the best fitting SOGGM were used. Details of individual model modifications to rectify estimation difficulties for Cohort 3 class solutions are in Table 4 of the supplemental materials.
Predictors by Trajectory Comparison
After selecting the unconditional model for Cohort 4, predictors of class membership were assessed by using two separate, supplemental analytic techniques: multinomial logistic regression and relative weights analysis. Using Mplus, we conducted multinomial logistic regression analyses following the three-step method developed by Vermunt (2010) and delineated by Asparouhov and Muthén (2013). This technique accounts for measurement error associated with assignment to latent classes while allowing class membership and trajectory structure to remain intact, which previous analysis strategies could not do.
The relative importance of predictors was determined using relative weights analysis. The goal was to examine the contribution each variable makes to the prediction of PTSD class membership both alone and in combination with the other variables in the model (Johnson & LeBreton, 2004). Multinomial logistic regression analysis fails to sufficiently account for predictor collinearity and is thus not ideal for determining the relative impact of predictors. Relative weights analysis more accurately partitions variance among correlated predictors by creating orthogonal factors that are maximally correlated with the original variables (LeBreton, Hargis, Griepentrog, Oswald, & Ployhart, 2007). We conducted relative weights analyses for logistic regression using an R (R Core Team, 2013) macro developed by Tonidandel and LeBreton (2010). This macro produces weights that are interpreted as a measure of relative effect size and confidence intervals that demonstrate whether the impact of a given predictor is significantly different from 0 (Tonidandel, LeBreton, & Johnson, 2009). It should be noted that although this analysis strategy more accurately partitions variance, unlike the multinomial logistic regression analysis conducted in Mplus, we were unable to compensate for measurement error in the assigned latent class membership. However, when measurement error is low as indicated by high entropy values, this analysis strategy produces relatively unbiased estimates.
Together, the multinomial logistic regression and the relative weights analysis provide information about the ability of predictors to account for variance in trajectory membership, but prediction does not necessarily translate into an ability to screen for trajectory membership, which might have greater practical significance in the military context. To test the classification rates of the predictors, we conducted a multivariable multinomial logistic regression analysis in SPSS (Version 20) where all predictor variables were entered simultaneously as predictors of trajectory membership.
Finally, we conducted two hierarchical multinomial logistic regression analyses in Mplus (Version 7.1) to separately test the interactive effects of combat exposure and prior lifetime trauma, and combat exposure and childhood trauma, on PTSD symptom course. Predictor variables were centered prior to analysis.
Results Multivariate Measurement Model
The strict measurement-invariant longitudinal factor model fit the data well, χ2(17, 867) = 59.78, p < .001 (Tucker Lewis index [TLI]: 0.976, comparative fit index [CFI]: 0.981, Root mean square error of approximation [RMSEA]: 0.054 [.039–.069], standardized root mean square residual [SRMR]: 0.032). Both CAPS and PCL scores were related to the latent construct of PTSD with standardized factor loadings across the four time points ranging from .88 to .96. The factor loadings for the CAPS were higher than the PCL at each time point in the original factor model, and were lower than the PCL in the full SOGMM.
SOGMM
Table 3 shows the fit statistics of the class solutions for the PTSD factor model. The BIC and SSA-BIC fit indices suggested relative improvement in fit with increasing number of classes. Significant LMR-LRTs, however, indicated the three- and four-class solutions were a comparatively better fit to the data. The three- and four-class solutions had high entropy (.82 and .95, respectively) and high average posterior probabilities (all >86%), indicating good classification. The four-class solution included a class populated by approximately 1% of the total count. Consequently, we selected the three-class solution as optimal.
In the three-class solution (see Figure 2), 677 Marines (78.1%) had relatively low symptoms over all time points (labeled low–stable symptoms), 108 (12.5%) had relatively increasing symptoms over the course of deployment (labeled new-onset symptoms), and 81 (9.4%) had relatively high symptoms at the initial, predeployment time point that decreased slightly across all subsequent assessments (labeled preexisting symptoms). Quadratic trends fit the data best for the low–stable symptoms trajectory (b = −1.57, SE = 0.18, p < .001) and the new-onset symptoms trajectory (b = −3.63, SE = 0.88, p < .001), whereas a linear trend best defined the preexisting symptoms trajectory (b = −3.13, SE = 0.77, p < .001).
Figure 2. Posttraumatic stress disorder (PTSD) severity over time by latent class for Cohort 4. CAPS = Clinician-Administered PTSD Scale.
Descriptive Statistics by Trajectory
Means and standard deviations of all study variables for each trajectory and for the full sample are reported in Table 2. PTSD cases were most prevalent in the new-onset symptoms trajectory for T1 through T3, and subthreshold cases were most prevalent in the new-onset symptoms trajectory for T2 and T3. The preexisting symptoms trajectory had the second-highest rates of PTSD and subthreshold PTSD at all postdeployment time points and the highest rates of PTSD and subthreshold PTSD at T0. Ratings of depression, anxiety, and functional impairment exhibited the same pattern, paralleling the severity of PTSD symptoms at each time point. Notably, history of previous deployments did not predict trajectory membership, χ2(2, 859) = 2.95, p = .229.
SOGMM Comparison in Cohort 3
The longitudinal factor model for Cohort 3 had an adequate fit to the data, χ2(17, 658) = 79.79, p < .001 (TLI: 0.957, CFI: 0.965, RMSEA: 0.075 [.059–.092], SRMR: 0.031), and both the CAPS and PCL scores were related to the latent construct of PTSD with standardized factor loadings across the four time points ranging from .85 to .97. For the SOGMM, the BIC and SSA-BIC fit indices suggest relative improvement in fit with increasing number of classes (see Supplemental Materials, Table 4). Significant LMR-LRTs indicated that the four-class solution was a comparatively better fit to the data; however, the four-class solution had one trajectory populated by only approximately 1% of the total sample. The three-class solution also had one trajectory populated by less than 5% of the sample, and this trajectory paralleled one of the other trajectories. In the two-class solution, the two parallel trajectories in the three-class solution collapsed into a single trajectory comprising 9%, of the total sample. Consequently, the two-class solution was selected because it provided the most parsimonious and interpretable solution.
In the two-class solution (see Figure 3), 602 Marines (91.5%) had relatively low symptoms over all time points (low–stable symptoms), and 56 (8.5%) had high symptoms at the initial, predeployment time point that decreased substantially across subsequent assessments (preexisting symptoms). A quadratic trend fit the data best for the low–stable symptoms trajectory (b = −0.32, SE = 0.10, p = .001) and a linear trend best defined the preexisting symptoms trajectory (b = 1.08, SE = 0.53, p = .040). Convergent outcomes, including rates of PTSD and subthreshold PTSD, PTSD severity, and ratings of depression, anxiety, and functional impairment, were generally as expected given the courses of the preexisting symptoms and the low–stable symptomstrajectories (see Supplemental Materials, Table 1).
Figure 3. Posttraumatic stress disorder (PTSD) severity over time by latent class for Cohort 3. CAPS = Clinician-Administered PTSD Scale.
Predictors of PTSD Symptom Trajectories in Cohort 4
New-onset PTSD symptoms versus preexisting PTSD symptoms
Multinomial logistic regression analysis revealed that compared with the preexisting symptoms group, participants in the new-onset symptoms trajectory experienced higher levels of combat exposure (b = 1.26, p = .001) and less prior lifetime trauma (LEC; b = −.15, p = .028; see Table 4). The relative weights analysis confirmed that combat exposure and prior lifetime trauma were two of the strongest predictors, accounting for 13.5% and 3.7% of the total variance, respectively. Collectively, all analyzed predictors accounted for 26.1% of the total variance.
New-onset PTSD symptoms versus low–stable symptoms
Multinomial logistic regression analysis revealed that Marines in the new-onset symptoms trajectory were more likely to report peritraumatic dissociation (b = 1.39, p < .001), use avoidant coping (b = 2.45, p = .001), and experience more prior lifetime trauma (b = .13, p = .036) than participants in the low–stable symptoms group. The results of the relative weights analysis mirrored these findings: peritraumatic dissociation accounted for 10.7% of the total variance, avoidant coping accounted for 8.0%. Prior lifetime trauma, however, was not significant. Collectively, all analyzed predictors accounted for 31.7% of the total variance.
Preexisting PTSD symptoms versus low–stable symptoms
Multinomial logistic regression analysis showed that compared when the low–stable symptoms group, participants in the preexisting symptoms trajectory reported more prior lifetime trauma (b = .28, p < .001), greater peritraumatic dissociation (b = 1.01, p = .002), and avoidant coping (b = 2.47, p < .001). In the relative weights analysis, prior lifetime trauma (7.9%), avoidant coping (5.2%), and peritraumatic dissociation (3.3%) each emerged as significant predictors. Collectively, all predictors together accounted for 20.1% of the total variance.
Classification rates of all predictors
Inspection of the classification tables in the multivariable logistic regression analyses revealed that all predictors simultaneously correctly classified 29.2% of those in the new-onset symptoms trajectory, 10.4% of those in the preexisting symptoms trajectory and 96.9% of those in the low–stable symptom trajectory suggesting the model has high specificity but low sensitivity. The model gave an overall classification success rate of 80.9%.
Interaction between prior trauma and combat exposure
Hierarchical multinomial logistic regressions failed to reveal a synergistic effect on trajectory membership of combat exposure and childhood trauma or combat exposure and prior lifetime trauma (see Table 5).
Synergistic Effects: Combat Exposure-by-Personal History
DiscussionWe examined the heterogeneity of PTSD symptom course in a cohort of concurrently deployed, highly combat-exposed Marines from pre- to early postdeployment throughout one deployment cycle. We aimed to elucidate the longitudinal patterns of posttraumatic stress symptoms that might be expected in the wake of a high-exposure deployment. Three trajectories of PTSD symptoms best fit the data, each representing subgroups of Marines: (a) a low–stable symptom trajectory characterized by persistently low symptoms; (b) a new-onset symptoms trajectory, consisting of Marines who reported clinically significant PTSD symptoms after deployment that had not existed prior to deployment; and (c) a preexisting symptoms trajectory characterized by high PTSD symptoms reported prior to deployment that gradually decreased but remained moderate through eight month postdeployment. A GMM analysis of a separate MRS cohort of deployed Marines replicated these trajectory patterns, except for the absence of the new-onset group. The lack of a new-onset PTSD trajectory in the validation cohort might be explained by their lower overall (unit-wide) level of stressor exposure during deployment.
Contrary to our expectations, a recovery trajectory was not modal in our primary sample. A low–stable symptom trajectory of persistently low PTSD symptoms across all data-collection time points was the most prevalent outcome (79%), suggesting that this trajectory is normative even among military service members with high levels of combat exposure. This low–stable symptom trajectory was similar to resilience trajectories found in previous studies of service members (e.g., Bonanno et al., 2012; Dickstein et al., 2010), except that in contrast to the flat resilience trajectories that were modal in them, Marines in our low–stable symptom group reported a clinically meaningful 10-point rise in PTSD symptom severity between T0 and T1 (e.g., Schnurr et al., 2007), followed by a decline in symptom severity postdeployment. The low–stable course in our study might have differed from similar trajectories in previous studies because our sample was arguably more uniformly exposed to significant stressor events, such that fewer participants in our study were unaffected. This finding contributes to the recent debate in the field about whether persistently low symptoms attributed to resilience might, in some cases, be confounded with low exposure (e.g., Bonanno, 2013; Steenkamp, Litz, Dickstein, Salters-Pedneault, & Hofmann, 2013). Although it is difficult to compare levels of combat exposure across studies because of methodological differences, the high combat fatality rate experienced by our cohort over the span of only 7 months in theater (17/867 = 1.9%) suggests a comparatively high level of combat exposure. In contrast, the U.S. Department of Defense’s Defense Casualty Analysis System (2013) reported that of 2,147,375 U.S. service members deployed to Iraq or Afghanistan between 2001 and 2010 (cited in Institute of Medicine, 2013), 4,585 (0.2%) were killed by hostile action, whereas the U.S. military casualty rate in the 1990–1991 Gulf War was 0.02% (Leland & Oboroceanu, 2010). Overall, our findings suggest that although the prevalence of persistently low symptoms might not be dependent on the degree of combat exposure (it remains the modal outcome), the degree of exposure appears to alter the form that this course takes; in our highly combat-exposed sample, resilience appeared to entail an elastic rebounding from meaningful posttraumatic stress symptoms rather than the absence of those symptoms.
The trajectory of most concern for military prevention and early intervention programs is the new-onset symptoms trajectory (12.5%), consisting of Marines who reported very low symptoms prior to deployment but persistently high symptoms at all postdeployment time points. At 1 month postdeployment, 83.6% of Marines in this group met criteria for full (61.2%) or subthreshold (22.4%) PTSD. Seven months later, the percentage meeting full criteria for PTSD had declined to 39.5%, but the percentage meeting criteria for subthreshold PTSD had increased to 47.5%, suggesting that 87% still reported clinically noteworthy symptomatology 8 months after deployment. The prevalence of our new-onset symptoms trajectory was higher than similar new-onset trajectories reported by Berntsen et al. (2012) and Bonanno et al. (2012), who found prevalence rates of 4% and 6.7% respectively, which might partially reflect differences in levels of combat exposure across studies. It is noteworthy that predeployment symptom levels were virtually identical for our low–stable symptom and new-onset symptoms trajectories suggesting, importantly, that screening for PTSD symptoms prior to a deployment would fail to predict the development of new-onset PTSD postdeployment.
The least prevalent trajectory derived from this GMM, preexisting symptoms (8%), comprised Marines reporting high PTSD symptoms immediately prior to deployment that gradually declined over the study period. At predeployment, 69% of Marines in this trajectory met criteria for full or subthreshold PTSD. By T3, this percentage had dropped to 50%, suggesting slight symptom improvement over the deployment cycle. Marines in this trajectory reported greater prior lifetime trauma than the other two trajectories (but levels of childhood trauma that were similar to the new-onset symptoms trajectory), suggesting that their symptoms were trauma-related rather than solely nonspecific distress or anticipatory anxiety (cf. Dickstein et al., 2010).
The trajectories we did not find might be as important as the trajectories we did. We found no recovery trajectory, which would have consisted of an initial deployment-related increase to superthreshold levels of distress followed by a gradual decrease toward baseline during the early postdeployment period. Dickstein et al. (2010) found a recovery trajectory making up 4% of their sample, and Bonanno et al. (2012) found a similar moderate–improving trajectory that made up 8% of their sample. Perhaps our cohort did not have a recovery trajectory because Marines in our study experienced higher levels of combat exposure than peace-keeping soldiers studied by Dickstein et al. (2010), and our postdeployment follow-up period was relatively short (8 months) compared with the 6-year follow-up reported by Bonanno et al. (2012). It is possible that our new-onset symptoms trajectory, given time, would bifurcate into chronic and recovery courses. We also did not find a delayed trajectory, perhaps again because our postdeployment follow-up was too brief; delayed PTSD, by definition, has an onset of at least 6 months after the trauma, but typically emerges years after the trauma (Andrews, Brewin, Stewart, Philpott, & Hejdenberg, 2009). Finally, unlike Bonanno et al. (2012), we also did not find a persistently chronic trajectory (persistently high symptoms across all time points; 2.2% in their sample), given that Marines in our preexisting symptoms group experienced a decrease in symptom severity over time.
Not surprisingly, combat exposure predicted membership in the new-onset symptoms trajectory relative to the preexisting symptoms course. However, contrary to prior cross-sectional and some longitudinal research, combat experiences did not distinguish the new-onset symptoms trajectory from the low–stable symptoms course. Also, perceived threat did not predict membership between any trajectory and prior trauma history (childhood trauma, prior lifetime trauma, and prior deployments) did not confer as much risk as has been found in previous cross-sectional studies (e.g., Cabrera, Hoge, Bliese, Castro, & Messer, 2007; Clancy et al., 2006; Dedert et al., 2009; Iversen et al., 2008). In addition, combat experiences did not interact with prior life span trauma to enhance risk. Also surprising, given the literature identifying unit cohesion and other sources of social support as significant protective factors for combat-related PTSD, neither unit support (perceived social support from peers and leaders during deployment) nor general postdeployment social support uniquely predicted trajectory membership. On the one hand, this null finding diminishes the validity of the putatively divergent courses found in this study. On the other hand, perhaps our findings differ from those of previous studies and appear to contradict models of deployment-related PTSD that assign primacy to combat experiences as a risk factor and to social support as a protective factor, because our sample comprised members of a single elite combat unit in which both stressor exposures and social support were likely both uniformly high, allowing for relatively less variation than existed in studies using other recruitment strategies. Also, perhaps our measures of social support were too coarse-grained to capture cohesive bonds that might impact stress outcomes in the military, such as trust under fire or shared meaning making. Because the combat exposure scale indexes only warzone dangers, future research should examine other important dimensions of combat exposure (e.g., loss, moral transgression). It might also be useful to augment service member reports of interpersonal behaviors, which might be biased, with family, leader, or peer observations.
Our finding that peritraumatic dissociation and avoidance coping were the strongest predictors of membership in the new-onset symptoms trajectory relative to the low–stable symptoms trajectory might lend support for the stress injury model in that both might serve as markers of stress that exceeded individual adaptive limits in Marines whose apparent risk might have been no greater than their peers. Both peritraumatic dissociation and avoidant coping might serve as targets for indicated prevention programs (e.g., Nash & Watson, 2012) through either self-report screening or observation by leaders, peers, family members, and care providers who are sufficiently familiar with service members to recognize changes in behaviors consistent with these processes.
Our study extended previous military GMM studies of PTSD in several ways. We employed both clinician-administered structured interviews and self-report questionnaires to assess PTSD symptoms, rather than relying solely on either. We assessed PTSD symptom burden at each data-collection time point in reference to participants’ currently most distressing event, regardless of its context, thereby not artificially constraining the trauma-related symptom burden in our sample. In contrast to previous longitudinal studies of service members, the cohort employed in our analyses was a relatively highly exposed group of U.S. military service members who deployed and returned together after performing a warzone mission that was arguably prototypical for the U.S. Global War on Terror: countering insurgency on the ground in a highly contested province of Afghanistan at the peak of conflict there.
Our findings converge with those of previous military GMM studies in two key areas, despite differences in methodology, population, and era. First, we confirmed that most service members report low symptoms throughout the deployment cycle (Bonanno et al., 2012; Berntsen et al., 2012; Dickstein et al., 2010); this was the case in both our primary (78%) and validation (91%) cohorts. Other investigators have argued that rates of psychopathology might be lower than expected among deployed service members because significantly symptomatic or at-risk service members might never have been sanctioned to deploy (Larson, Highfill-McRoy, & Booth-Kewley, 2008; Wilson, Jones, Fear, Hull, Hotopf, Wessely, & Rona, 2009). Our study also confirmed the findings of other GMM studies in service members that PTSD symptoms that are high prior to deployment do not necessarily worsen as a result of a combat deployment, despite the literature suggesting that prior trauma history (e.g., Dedert et al., 2009; LeardMann, Smith, & Ryan, 2010) and predeployment PTSD symptom burden (Franz et al., 2013) can be significant risk factors for postdeployment PTSD symptoms. Perhaps deployment-related protective factors might mitigate the risk posed by preexisting PTSD symptoms, such as unit cohesion, military training, and an increased sense of purpose and meaning while deployed (e.g., Rona et al., 2009).
Findings across longitudinal studies highlight the complex role played by combat exposure on PTSD symptom course and the limitations of traditional dose-response conceptualizations. We confirmed the findings of Orcutt et al. (2004) and Bonanno et al. (2012) that service members who experienced significant worsening of PTSD symptoms from predeployment to postdeployment endorsed the highest levels of combat exposure. On the other hand, peritraumatic dissociation and avoidant coping were stronger predictors of PTSD course than combat exposure, suggesting that deployed service members’ overall stressor dose might be less salient than how they were affected by those stressors. More research is needed to better understand the relationships between person-specific predictors, such as peritraumatic dissociation and avoidant coping and environment-specific predictors such as stressor exposures and social support.
Several limitations of our study deserve mentioning. Because Marines were not randomly selected for inclusion in the study, and our sample included no women, the generalizability of our results is uncertain. In particular, attrition and reranging of data resulted in significant missing data and affected the representativeness of the sample; Marines excluded because of attrition were older and more likely to have previously deployed, and had greater functional impairment and more prior lifetime trauma. The siphoned sample also limited the number of predictors that could be examined, and it prevented detection of potentially finer-grained differences between smaller proportioned trajectories. The duration of our postdeployment follow-up was too short to predict long-term PTSD symptom burdens. Finally, in practical terms, the predictors we chose were good at identifying Marines who are able to bounce back over the deployment cycle but not at predicting those at risk to develop enduring postdeployment PTSD.
In the Method section, we highlighted limitations of using mixture models and the steps we took to minimize these limitations. Notably, we used a second-order model to minimize the impact of poor measurement and the identification of spurious classes. Also, we substantiated our class solutions by testing covariates, which distinguished classes as anticipated. In addition, we were able to compare class solutions in Cohort 4 with those in Cohort 3, partly replicating our results. Nevertheless, our findings need to be confirmed by other prospective studies of highly exposed service members using various statistical approaches to modeling intra-individual change.
Alternative approaches to examining differential trajectories of adaptation to warzone exposure that should be explored include multiple group growth models, structural equation modeling trees with a growth model (Brandmaier, von Oertzen, McArdle, & Lindenberger, 2013), and recursive partitioning with longitudinal data (Abdolell, LeBlanc, Stephens, & Harrison, 2002). Multiple group growth models allow for in-depth examinations of group differences in change trajectories but require a priori classification of individuals into groups. Structural equation modeling trees and recursive partitioning with longitudinal data, like growth mixture models, are exploratory approaches to studying group differences in longitudinal trajectories. They create classes of change over time through repeated binary splits on observed variables to determine which individuals change in similar ways and which change in disparate ways. Finally, it is also possible that analyses extracting a single continuous intercept and slope might be at least as useful as those that recover trajectory groups.
ConclusionIn this article, we examined heterogeneity in PTSD symptom course using GMM in a sample of highly combat-exposed U.S. Marines. We found that three symptom trajectories best characterized the data and that peritraumatic dissociation and avoidant coping (person-level variables indexing trauma-related perceptions and behaviors) best distinguished these trajectories. We repeated our methodology in a separate cohort of less highly combat exposed Marines, in whom we found similar trajectories except for the absence of a new-onset course. Overall, our findings revealed that in highly exposed U.S. Marines, limited and temporary PTSD symptoms might be the most prevalent course, and that significant changes in PTSD symptoms from predeployment to 1-month postdeployment might provide the best indicator of ultimate first-year course.
Footnotes 1 A one-way ANOVA and a post hoc Tukey’s test revealed that Cohort 4 had significantly higher combat exposure than had all other cohorts, F(3, 2205) = 371.87, p < .001.
2 Additional steps for preliminary model testing were prohibited by the missing CAPS variable. Strict measurement invariance was necessary to identify the model, preventing tests of the progressively invariant measurement models recommended. Separate comparable GMMs for each indicator were also not possible.
3 We attempted to use a latent basis GMM to elucidate variability across class solutions, but the added complexity of these models prohibited an identifiable solution.
4 Whereas FIML addresses missingness on the dependent variable, it does not address predictor missingness. Generally, multiple imputation is used to address predictor missingness, but because significance testing of relative weights analysis prohibits the use of multiple imputation (Shao & Sitter, 1996) to keep predictor analyses comparable, participants missing data on any predictor were deleted from all predictor analyses.
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Submitted: February 8, 2014 Revised: October 5, 2014 Accepted: October 6, 2014
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Source: Journal of Abnormal Psychology. Vol. 124. (1), Feb, 2015 pp. 155-171)
Accession Number: 2014-49228-001
Digital Object Identifier: 10.1037/abn0000020
Record: 40- Title:
- Posttreatment motivation and alcohol treatment outcome 9 months later: Findings from structural equation modeling.

- Authors:
- Cook, Sarah. London School of Hygiene & Tropical Medicine, London, United Kingdom, sarah.cook@lshtm.ac.uk
Heather, Nick. Department of Psychology, Faculty of Health and Life Sciences, Northumbria University, United Kingdom
McCambridge, Jim. London School of Hygiene & Tropical Medicine, London, United Kingdom - Address:
- Cook, Sarah, London School of Hygiene & Tropical Medicine, Keppel Street, London, United Kingdom, WC1E 7HT, sarah.cook@lshtm.ac.uk
- Source:
- Journal of Consulting and Clinical Psychology, Vol 83(1), Feb, 2015. pp. 232-237.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 6
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- treatment, motivation, alcohol problems, readiness to change, outcome predictors
- Abstract (English):
- Objective: To investigate the association between posttreatment motivation to change as measured by the Readiness to Change Questionnaire Treatment Version and drinking outcomes 9 months after the conclusion of treatment for alcohol problems. Method: Data from 392 participants in the United Kingdom Alcohol Treatment Trial were used to fit structural equation models investigating relationships between motivation to change pre- and posttreatment and 5 outcomes 9 months later. The models included pathways through changes in drinking behavior during treatment and adjustment for sociodemographic information. Results: Greater posttreatment motivation (being in action vs. preaction) was associated with 3 times higher odds of the most stringent definition of positive outcome (being abstinent or entirely a nonproblem drinker) 9 months later (odds ratio = 3.10, 95% confidence interval [1.83, 5.25]). A smaller indirect effect of pretreatment motivation on this outcome was seen from pathways through drinking behavior during treatment and posttreatment motivation (probit coefficient = 0.08, 95% confidence interval [0.03, 0.14]). A similar pattern of results was seen for other outcomes evaluated. Conclusion: Posttreatment motivation to change has hitherto been little studied and is identified here as a clearly important predictor of longer term treatment outcome. (PsycINFO Database Record (c) 2018 APA, all rights reserved)
- Impact Statement:
- What is the public health significance of this article?—This study found that those individuals who reported that they were ready to change their drinking at the end of a treatment program were much more likely to show positive outcomes 9 months subsequently than were persons not indicating such a readiness to change. This suggests that attempting to enhance motivation throughout the process may be an important component of successful alcohol treatment. (PsycINFO Database Record (c) 2018 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Alcohol Rehabilitation; *Motivation; *Readiness to Change; *Structural Equation Modeling; *Treatment Outcomes
- Medical Subject Headings (MeSH):
- Adult; Alcoholism; Female; Follow-Up Studies; Great Britain; Humans; Male; Motivation; Surveys and Questionnaires; Treatment Outcome
- PsycINFO Classification:
- Drug & Alcohol Rehabilitation (3383)
- Population:
- Human
Male
Female - Location:
- United Kingdom
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Leeds Dependence Questionnaire
Readiness to Change Questionnaire—Treatment Version DOI: 10.1037/t01789-000
Alcohol Problems Questionnaire DOI: 10.1037/t61043-000 - Grant Sponsorship:
- Sponsor: Medical Research Council
Grant Number: G9700729
Other Details: United Kingdom Alcohol Treatment Trial was funded by the aforementioned sponsor.
Recipients: No recipient indicated
Sponsor: Sponsor name not included
Grant Number: WT086516MA
Other Details: Wellcome Trust Research Career Development Fellowship in Basic Biomedical Science
Recipients: McCambridge, Jim - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Sep 22, 2014; Accepted: Aug 4, 2014; Revised: May 8, 2014; First Submitted: Mar 8, 2013
- Release Date:
- 20140922
- Correction Date:
- 20180215
- Copyright:
- The Author(s). 2014
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0037981
- PMID:
- 25244390
- Accession Number:
- 2014-39094-001
- Number of Citations in Source:
- 25
- Persistent link to this record (Permalink):
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- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-39094-001&site=ehost-live">Posttreatment motivation and alcohol treatment outcome 9 months later: Findings from structural equation modeling.</A>
- Database:
- PsycINFO
Posttreatment Motivation and Alcohol Treatment Outcome 9 Months Later: Findings From Structural Equation Modeling / BRIEF REPORT
By: Sarah Cook
London School of Hygiene & Tropical Medicine;
Nick Heather
Department of Psychology, Faculty of Health and Life Sciences, Northumbria University
Jim McCambridge
London School of Hygiene & Tropical Medicine
On Behalf Of: the UKATT Research Team
Acknowledgement: The United Kingdom Alcohol Treatment Trial was funded by the Medical Research Council (Project Grant G9700729). Jim McCambridge is supported by a Wellcome Trust Research Career Development Fellowship in Basic Biomedical Science (WT086516MA). The authors have no conflicts of interest.
Identifying how motivation to change is related to positive outcomes is important for understanding how treatment for alcohol or other behavioral problems can work. Pretreatment stage of change has been found to be an important predictor of outcome for a wide range of disorders (Norcross, Krebs, & Prochaska, 2011), including several aspects of treatment for alcohol problems (Connors et al., 2000; Hernandez-Avila, Burleson, & Kranzler, 1998; Isenhart, 1997; Project MATCH Research Group, 1998).
Three studies have previously investigated three different motivational measures, all based on stages of change, assessed at the conclusion of treatment for alcohol problems. A profile analysis generated from stage of change variables in Project MATCH identified a relationship between more strongly endorsing action posttreatment, measured using the University of Rhode Island Change Assessment (DiClemente & Hughes, 1990), and longer term abstinence (Carbonari & DiClemente, 2000). Another analysis of data from two cognitive behavior therapy alcohol treatments for women found posttreatment motivation, as measured by the Stages of Change Readiness and Treatment Eagerness Scale (Miller & Tonigan, 1996), was a mediator of the relationship between social support for drinking and drinking frequency 6 months later (Hunter-Reel, McCrady, Hildebrandt, & Epstein, 2010).
A third study that was based on data from the United Kingdom Alcohol Treatment Trial (UKATT; UKATT Research Team, 2005) found that posttreatment, but not pretreatment, stage of change, measured by the Readiness to Change Questionnaire Treatment Version (Heather & Hönekopp, 2008), was predictive of drinking outcomes at follow-up 9 months after treatment ended (Heather & McCambridge, 2013). The associations in this study were greatly reduced after adjusting for drinking behavior during treatment: Effect sizes were smaller and did not obtain statistical significance on the most stringent definitions of positive outcome (Heather & McCambridge, 2013). However, motivation to address alcohol problems will be highly interconnected with drinking behaviors before, during, and after treatment, making study of their effects complex (Rollnick, 1998).
The conceptual framework guiding the present study posits that interconnected pathways between variables are structured by time and that mediator and moderator variables may have proximal or distal impacts on one another, the strength of which may vary with time. Structural equation modeling is a flexible statistical technique that can be used to analyze interconnected pathways between variables and thus provide more detailed information on their relationships throughout the treatment process. Our primary hypothesis was that motivation to change drinking posttreatment predicts outcome of treatment for alcohol problems. Our aim in this study was to use structural equation modeling to further investigate the associations previously observed in the UKATT data between posttreatment motivation to change drinking and drinking outcomes roughly 9 months after the conclusion of treatment (Heather & McCambridge, 2013), including delineation of pathways through changes in drinking behaviors, paying careful attention to temporal sequencing in the context of an explicitly longitudinal perspective on change.
Method Study Sample and Design
The UKATT (UKATT Research Group, 2005) was a multicenter randomized controlled trial carried out in five treatment centers in the United Kingdom that compared two different treatments for alcohol problems: motivational enhancement therapy (MET) and social behavior and network therapy (SBNT). This was a pragmatic trial and the study population comprised clients who would normally receive an offer of treatment for alcohol problems in publicly funded treatment services in the United Kingdom. No differences were found between the two treatment groups on any of the drinking outcomes (UKATT Research Team, 2005). Motivation to change and drinking behaviors were measured pretreatment and then at 3 months (when all treatment was ended) and 9 months later, that is, 12 months after entry to the trial. UKATT recruited 742 clients (MET = 422, SBNT = 320) attending treatment voluntarily. Because our research question was related to treatment process, only clients who attended at least one session were included (n = 590). We also examined those with complete data available on the variables of interest at all three time points because the aim of this study was to model the interrelationship between these variables over time. This resulted in a sample of 392 clients included for the present study. There were some differences between this subsample and those who were not included in terms of education (those included were more likely to have been educated to degree level or equivalent [12.2% vs. 7.4%, p = .036] and less likely to have no educational qualification [30.4% vs. 41.7%, p = .002]). The included subsample also had somewhat less severe problems at baseline (lower mean scores on the Leeds Dependence Questionnaire [15.1 vs. 16.4, p = .030] and Alcohol Problems Questionnaire [10.4 vs. 11.7, p < .001]).
Measures
Outcome variables were derived from Form 90 data on alcohol consumption in the past 90 days (Miller, 1996) and the Alcohol Problems Questionnaire (APQ; Drummond, 1990). Data from the Form 90 and APQ were combined to derive three binary treatment outcome variables based on a composite categorical variable developed by Heather and Tebbutt (1989):
- Outcome 1: Abstinent or nonproblem drinker (no alcohol consumption in the past 90 days or drinking with a score of zero on the APQ indicating no evidence of any alcohol problems)
- Outcome 2: At least much improved (abstinent or drinking with a reduction in APQ score from baseline to follow up of at least two thirds)
- Outcome 3: At least somewhat improved (abstinent or drinking with a reduction in APQ score from baseline of at least one third)
These outcomes are principally concerned with the resolution of alcohol problems and vary in the stringency of the definition of a positive outcome. The additional outcomes investigated were two continuous measures of drinking behavior derived from the Form 90 data:
- Outcome 4: Drinks per drinking day (DDD) in the past 90 days, with abstinent clients given a score of zero
- Outcome 5: Percentage of days abstinent (PDA)
These were the same outcome measures used by
Heather and McCambridge (2013).
Motivation to change was assessed using the revised edition of the Readiness to Change Questionnaire Treatment Version, which is designed for use in alcohol-treatment seeking populations (Heather & Hönekopp, 2008) and which refers to both quitting and cutting down on alcohol consumption. This 12-item instrument was used to calculate scores on three stages of change: precontemplation, contemplation, and action. Clients are assigned a stage of change based on the scale on which they score highest, with ties being decided in favor of the stage farthest along the cycle of change. As no clients were in the precontemplation stage at pretreatment and only three were at posttreatment, we defined actively changing drinking (action stage) versus not actively changing drinking (precontemplation + contemplation stages = preaction) as a binary variable.
Sociodemographic variables measured pretreatment were age (coded into 5-year groups), education (coded as no qualifications, some qualifications, and degree or equivalent qualifications), and marital status (married and/or cohabiting or not). Pretreatment score on the Leeds Dependence Questionnaire (Raistrick et al., 1994) assessing the severity of dependence at treatment entry was also included in the model as a predictor of drinking behavior during treatment.
Statistical Analyses
The relationship between motivation to change (pre- and posttreatment) and treatment outcomes 9 months posttreatment was assessed using the structural equation model shown in Figure 1 for Outcome 1. This model was specified a priori by considering the likely temporal relationship between variables. For example, effects of pretreatment motivation to change on drinking outcomes at 9 months posttreatment were considered to be through effects on intermediate variables (drinking during treatment and posttreatment motivation to change). This hypothesis was tested by adding in a direct effect of pretreatment motivation to change on treatment outcomes at 9 months in a sensitivity analysis.
Figure 1. Structural equation model examining relationship between actively changing drinking following treatment and long term drinking outcomes. N = 392. Coefficients are linear regression coefficients for continuous outcomes and probit coefficients for binary outcomes. SBNT = social behavior and network therapy; MET = motivational enhancement therapy; CFI = comparative fit index; TLI = Tucker–Lewis index; RMSEA = root-mean-square error of approximation.
The model is divided by time into four sections—pretreatment, within treatment, posttreatment, and 9 months follow-up—to help elucidate the interrelationships between variables over time. Being abstinent or a nonproblem drinker (Outcome 1) was identified a priori as the main treatment outcome of interest, with models also fitted for the other two binary outcomes comprising less stringent definitions of positive outcome and the continuous outcomes (PDA and DDD) at 9 months posttreatment. Models were fitted separately for each outcome but with the same specified associations between the pre- and posttreatment variables because there was no reason to believe these relationships would differ between treatment outcomes. All models were adjusted for potential confounding by sociodemographic variables (age, sex, education, and marital status). Study site was also included as a confounder as this could represent both differences related to treatment services and geographic location.
Models were estimated using weighted least squares with mean and variance adjusted (WLSMV) but with maximum-likelihood estimation used to calculate odds ratios for the direct effects of posttreatment stage of change on binary drinking outcomes at 12 months. Model fit was assessed using the comparative fit index (CFI), Tucker–Lewis index (TLI), and the root-mean-square error of approximation (RMSEA). CFI and TLI values greater than .95 indicate good model fit, with a minimum of .90 indicating acceptable fit (Streiner, 2006; Tabachnik & Fidell, 1996). For the RMSEA, values greater than 0.10 indicate a bad fit, whereas values less than 0.08 indicate a reasonable fit and less than 0.05 indicate a good fit (Streiner, 2006).
ResultsThe study sample included 392 clients (74.7% male). Mean age was 42.2 years (SD 9.9). 46 clients were nonproblem drinkers at 9 months follow up, and 55 were abstinent. Overall, 153/392 clients overall met the criteria for being much improved and 225/392 were at least somewhat improved.
The results for the most stringent definition of positive treatment outcome (Outcome 1, abstinent/nonproblem drinker at 9 months) are shown in Figure 1. Model fit was very good. Greater posttreatment motivation (being in action vs. preaction) was associated with 3.10 (95% CI [1.83, 5.25]) higher odds (equivalent probit coefficient = 0.44, 95% CI [0.29, 0.59]) of positive outcome at 9 months. There was also good evidence for an indirect effect of pretreatment motivation on being abstinent or a nonproblem drinker at 9 months via effects on DDD and PDA at 3 months and posttreatment motivation (probit coefficient = 0.08, 95% CI [0.03, 0.14]). This was not reduced by including a direct effect of pretreatment motivation on treatment outcome in the model. There was no evidence for a direct effect of pretreatment motivation (probit coefficient = −0.19, 95% CI [−0.10, 0.48]).
The same pattern of results was seen for Outcome 2 (at least much improved; odds ratio for posttreatment motivation = 2.84, 95% CI [1.85, 4.38], and probit coefficient for indirect effect of pretreatment motivation = 0.09, 95% CI [0.03, 0.16]) and for Outcome 3 (at least somewhat improved; odds ratio for posttreatment motivation = 3.27, 95% CI [2.21, 4.84], and probit coefficient for indirect effect of pretreatment motivation = 0.11, 95% CI [0.04, 0.18]). Model fit for Outcomes 2 and 3 was reasonable (for Outcome 2, CFI = .95, TLI = .72, RMSEA = 0.06; for Outcome 3, CFI = .93, TLI = .60, RMSEA = 0.08).
Findings were also similar for Outcomes 4 and 5. Drinks per drinking day at 9 months were 4.14 (95% CI [3.45, 4.82]) fewer in those in action versus preaction posttreatment and 0.93 (95% CI 0.31, 1.55) drinks fewer in those in action versus preaction pretreatment. Those in action versus preaction posttreatment had 12.03% (95% CI [9.11, 14.95]) more abstinent days during Months 10–12. Those in action versus preaction at the beginning of treatment had 3.19% (95% CI [0.86, 5.52]) more abstinent days. Models for continuous outcomes had poorer model fit (for DDD, CFI = 0.64, TLI = −0.88, RMSEA = 0.19; for PDA, CFI = .83, TLI = .12, RMSEA = 0.12).
There was no evidence for Outcomes 2–5 of any direct effects of pretreatment motivation to change. Estimates for indirect effects of pretreatment motivation to change on treatment outcome did not substantively change by adding in a direct effect to the model for any of the treatment outcomes.
In contrast to all previous UKATT findings, there was some evidence (p < .05) of a treatment effect: Those who received SBNT were more likely than those in the MET group to be actively trying to change their drinking at the end of treatment for three out of five of the treatment outcomes (Outcomes 1, 2, and 5).
DiscussionMotivation to change, comparing those in action versus preaction at the conclusion of treatment for alcohol problems, was strongly associated with being abstinent or a nonproblem drinker at follow-up 9 months after treatment ended, approximately trebling the odds of this outcome. Pretreatment motivation had a lesser but nonetheless statistically significant indirect effect via effects on drinking behavior during treatment and posttreatment motivation. The same pattern of results was found for all other longer term treatment outcomes. These results, using a more sophisticated modeling approach, support and extend previous analyses of the same data set (Heather & McCambridge, 2013) by producing a more precise and indeed larger estimate of the effect of posttreatment motivation. Unlike the previous study, our study reveals an indirect effect of pretreatment motivation under the assumptions of no unmeasured confounders (Muthèn, 2011; VanderWeele, 2012) and no direct paths from the measured pretreatment variables to the outcomes, which shows the importance of considering change over time. Using a structural equation modeling approach enabled us to estimate more realistically the relationships between drinking behavior and motivation to change throughout the entire study period, taking account of the temporal nature of likely associations. These data add to the meager literature, comprising only two other treatment cohorts, for which different motivational measures were used.
This study used a binary motivational measure because almost all clients providing data at all three time points were in either the contemplation or the action stage of change, both pre- and posttreatment, and therefore there seemed little added benefit in using a more complex measure. The subsample used in this study had slightly less severe alcohol problems than did the overall UKATT study sample, which was broadly representative of the U.K. treatment population at the time the study was undertaken (Heather & McCambridge, 2013; UKATT Research Team, 2005), with implications for the generalizability of these data. Although measured motivation at treatment entry was similar among members of this group and the group not included in this study, the need to include those who attended at least one treatment session and also provided follow-up data posttreatment and at 12 months may mean there was differential loss to follow-up by motivation postrandomization, although it is difficult to assess this. Using a binary measure of motivation and restricting analyses to a subgroup of the UKATT population thus entails restrictions on the capacity to make inferences about the entire treatment population. In addition, although we have used here the Readiness to Change Questionnaire to measure motivation, there are different constructs and measures of motivation, including those not based on the stages of change (see Gaume, Bertholet, Daeppen, & Gmel, 2013). There is, therefore, a need for replication of analyses using different measures of motivation to fully understand motivation’s importance in treatment for alcohol problems.
These findings describe how drinking behavior changes over time and, notwithstanding that temporal sequencing rules out reverse causality, we make no direct causal inferences from these data given the observational nature of this study. Drinking measures for the 90 days prior to treatment (PDA and DDD) predict these same measures for the period during treatment. Pretreatment motivation also strongly predicts both of these measures during treatment. Reducing drinking is then associated with posttreatment motivation, which, in turn, predicts outcome 9 months later. If there is an underlying causal chain, capitalizing on motivation at the beginning of treatment and making progress during treatment thus appears important to longer term outcome, as is how treatment ends for clients and specifically their motivation to change their drinking at that point.
There was somewhat consistent evidence of a small treatment effect on posttreatment motivation favoring SBNT over MET. This counterintuitive finding could be explained if increased social support for change elicited by SBNT is more effective in motivating change efforts by the client than the mainly psychological processes targeted by MET. However, our finding contrasts with the previously reported analyses of UKATT outcomes, including no differences in the proportion of clients in the action stage of change posttreatment by treatment group (Heather & McCambridge, 2013). The reasons for these differences are not clear and further investigation is warranted.
Posttreatment motivation has been found to be a mediator of the relationship between baseline social support and drinking outcomes (Hunter-Reel et al., 2010). However, as far as we are aware, formal analyses of the role of motivation as a possible mediator of treatment effects has not been undertaken in alcohol treatment studies. In a related area, a brief motivational intervention was found to be more effective in decreasing negative drinking consequences through changes in motivation among emergency department attendees with injuries only in those who were already motivated to change before intervention (Stein et al., 2009). Further process studies are needed to test hypotheses about mediation and moderation of the effects of treatment for alcohol problems.
Korcha, Polcin, Bond, Lapp, and Galloway (2011) have drawn attention to the surprising absence of a longitudinal perspective on motivation in almost all existing alcohol and drug research, despite studies showing its importance for other behaviors such as smoking cessation (e.g., Boardman, Catley, Mayo, & Ahluwalia, 2005). Although it is possible that the lack of prior published studies may, to some degree, reflect publication bias, with null findings not reported, it is clear that posttreatment motivation is a neglected target for study in relation to treatment outcome. Further investigation of this somewhat novel candidate for mechanisms of behavior change and the application of a longitudinal perspective more broadly have potential for deepening the understanding of how alcohol treatment works.
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Submitted: March 8, 2013 Revised: May 8, 2014 Accepted: August 4, 2014
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Source: Journal of Consulting and Clinical Psychology. Vol. 83. (1), Feb, 2015 pp. 232-237)
Accession Number: 2014-39094-001
Digital Object Identifier: 10.1037/a0037981
Record: 41- Title:
- Predictive validity of callous–unemotional traits measured in early adolescence with respect to multiple antisocial outcomes.
- Authors:
- McMahon, Robert J.. Department of Psychology, University of Washington, Seattle, WA, US, robert_mcmahon@sfu.ca
Witkiewitz, Katie. Department of Psychology, Washington State University, Pullman, WA, US
Kotler, Julie S.. Department of Psychiatry & Behavioral Medicine, Seattle Children’s Hospital Research Institute, University of Washington, Seattle, WA, US - Institutional Authors:
- The Conduct Problems Prevention Research Group
- Address:
- McMahon, Robert J., Simon Fraser University, 8888 University Drive, Burnaby, BC, Canada, V5A 1S6, robert_mcmahon@sfu.ca
- Source:
- Journal of Abnormal Psychology, Vol 119(4), Nov, 2010. Oppositional Defiant Disorder and Conduct Disorder: Building an Evidence Base for DSM-5. pp. 752-763.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 12
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- Antisocial Process Screening Device, antisocial personality disorder, callous–unemotional traits, delinquency, predictive validity
- Abstract:
- This study investigated the predictive validity of youth callous–unemotional (CU) traits, as measured in early adolescence (Grade 7) by the Antisocial Process Screening Device (APSD; Frick & Hare, 2001), in a longitudinal sample (N = 754). Antisocial outcomes, assessed in adolescence and early adulthood, included self-reported general delinquency from 7th grade through 2 years post–high school, self-reported serious crimes through 2 years post–high school, juvenile and adult arrest records through 1 year post–high school, and antisocial personality disorder symptoms and diagnosis at 2 years post–high school. CU traits measured in 7th grade were highly predictive of 5 of the 6 antisocial outcomes—general delinquency, juvenile and adult arrests, and early adult antisocial personality disorder criterion count and diagnosis—over and above prior and concurrent conduct problem behavior (i.e., criterion counts of oppositional defiant disorder and conduct disorder) and attention-deficit/hyperactivity disorder (criterion count). Incorporating a CU traits specifier for those with a diagnosis of conduct disorder improved the positive prediction of antisocial outcomes, with a very low false-positive rate. There was minimal evidence of moderation by sex, race, or urban/rural status. Urban/rural status moderated one finding, with being from an urban area associated with stronger relations between CU traits and adult arrests. Findings clearly support the inclusion of CU traits as a specifier for the diagnosis of conduct disorder, at least with respect to predictive validity. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Antisocial Personality Disorder; *Juvenile Delinquency; *Screening; Statistical Validity
- Medical Subject Headings (MeSH):
- Adolescent; Aggression; Antisocial Personality Disorder; Child; Conduct Disorder; Crime; Emotions; Humans; Violence
- PsycINFO Classification:
- Behavior Disorders & Antisocial Behavior (3230)
- Population:
- Human
Male
Female - Location:
- US
- Tests & Measures:
- Self-Report of Delinquency
Antisocial Process Screening Device DOI: 10.1037/t00032-000 - Grant Sponsorship:
- Sponsor: National Institute of Mental Health, US
Grant Number: R18 MH48043; R18 MH50951; R18 MH50952; R18 MH50953; K05MH00797; K05MH01027; R01MH050951-15S1
Recipients: No recipient indicated
Sponsor: Center for Substance Abuse Prevention
Recipients: No recipient indicated
Sponsor: National Institute on Aging, US
Other Details: Fast Track through a memorandum of agreement with the NIMH
Recipients: No recipient indicated
Sponsor: US Department of Education, US
Grant Number: S184U30002
Recipients: No recipient indicated - Conference:
- Meeting of the Society for Research on Adolescence, Mar, 2010, Philadelphia, PA, US
- Conference Notes:
- Portions of this article were presented at the aforementioned conference.
- Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Oct 11, 2010; Accepted: Apr 15, 2010; Revised: Apr 13, 2010; First Submitted: Jul 13, 2009
- Release Date:
- 20101011
- Correction Date:
- 20120827
- Copyright:
- American Psychological Association. 2010
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0020796
- PMID:
- 20939651
- Accession Number:
- 2010-21298-001
- Number of Citations in Source:
- 86
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2010-21298-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2010-21298-001&site=ehost-live">Predictive validity of callous–unemotional traits measured in early adolescence with respect to multiple antisocial outcomes.</A>
- Database:
- PsycINFO
Predictive Validity of Callous–Unemotional Traits Measured in Early Adolescence With Respect to Multiple Antisocial Outcomes
By: Robert J. McMahon
Department of Psychology, University of Washington;
Katie Witkiewitz
Department of Psychology, Washington State University
Julie S. Kotler
Department of Psychiatry & Behavioral Medicine, Seattle Children's Hospital Research Institute, University of Washington
Acknowledgement: Members of the Conduct Problems Prevention Research Group, in alphabetical order, include Karen L. Bierman, Department of Psychology, Pennsylvania State University; John D. Coie, Department of Psychology, Duke University; Kenneth A. Dodge, Center for Child and Family Policy, Duke University; Mark T. Greenberg, Department of Human Development and Family Studies, Pennsylvania State University; John E. Lochman, Department of Psychology, The University of Alabama; Robert J. McMahon, Department of Psychology, University of Washington; and Ellen E. Pinderhughes, Department of Child Development, Tufts University.
Portions of this article were presented at the March 2010 meeting of the Society for Research on Adolescence, Philadelphia, Pennsylvania. This work was supported by National Institute of Mental Health (NIMH) Grants R18 MH48043, R18 MH50951, R18 MH50952, and R18 MH50953. The Center for Substance Abuse Prevention and the National Institute on Drug Abuse also have provided support for Fast Track through a memorandum of agreement with the NIMH. This work was also supported in part by Department of Education Grant S184U30002 and NIMH Grants K05MH00797, K05MH01027, and R01MH050951-15S1. For additional information concerning Fast Track, see http://www.fasttrackproject.org.
We are grateful for the close collaboration of the Durham Public Schools, the Metropolitan Nashville Public Schools, the Bellefonte Area Schools, the Tyrone Area Schools, the Mifflin County Schools, the Highline Public Schools, and the Seattle Public Schools. We greatly appreciate the hard work and dedication of the many staff members who implemented the project, collected the evaluation data, and assisted with data management and analyses. We also appreciate the consultation provided by Paul J. Frick and Patrick J. Curran on an earlier version of the manuscript.
In the past several decades, a wide range of risk factors has been identified that is associated with the development and persistence of conduct problems in children and adolescents. Additionally, a growing body of longitudinal data has demonstrated that there is substantial heterogeneity in the developmental trajectories leading to conduct problem behavior (see Lahey, Moffitt, & Caspi, 2003; McMahon, Wells, & Kotler, 2006, for reviews). Increased understanding of these trajectories has contributed to a more accurate conceptualization of youth conduct problems, which, in turn, has provided a foundation for more successful intervention and prevention efforts.
However, even with this progress, youth conduct problems (which frequently result in disruptions at home and school and can also lead to crime and violence) continue to represent a serious and costly societal problem (e.g., Aos, Lieb, Mayfield, Miller, & Pennucci, 2004; M. A. Cohen, 1998). Thus, conduct problem behaviors and their sequelae have continued to be a focus of public concern and a priority for the field of psychology (e.g., Dodge, 2008). In this context, researchers have continued efforts to identify new causal factors and developmental pathways, especially for youth with severe and often early-onset conduct problems who have not consistently responded to currently available treatments and preventive efforts.
Some researchers have looked to the adult literature to identify constructs that have been useful in conceptualizing and predicting antisocial behavior, with the assumption that these constructs might first appear in childhood and/or adolescence and might also be important in identifying unique etiological pathways to severe youth conduct problems. One such construct, psychopathy, has been extensively studied in adults (e.g., Cleckley, 1976; Hart & Hare, 1997; see Patrick, 2006, for a review). Traditional descriptions of the psychopathy construct include interpersonal aspects (e.g., superficial charm, grandiosity, manipulation, and lying), affective aspects (e.g., shallow emotions, callousness, and lack of guilt and empathy), and a behavioral dimension (e.g., impulsivity, irresponsibility, need for excitement, using others, and lack of realistic long-term goals; Cooke & Michie, 2001). In samples of adults, psychopathic traits predict a particularly serious and violent pattern of antisocial behavior that has been shown to be quite resistant to treatment (e.g., Hart, Kropp, & Hare, 1988; Patrick, 2006; Serin, 1993). Furthermore, the antisocial behavior associated with psychopathy in adults is widely thought to have a relatively different etiology from antisocial behavior in nonpsychopathic adults (e.g., Lykken, 1995).
These findings in adult populations prompted interest in whether conduct problems in some youth might be explained by a similar “youth psychopathy” correlate (e.g., Moffitt, Caspi, Dickson, Silva, & Stanton, 1996). Just as this discussion began to take hold, Lynam (1996, 1997, 1998) proposed that psychopathy, widely theorized to be a personality attribute, ought to be recognizable prior to adulthood. Additionally, citing evidence that attempts to treat psychopathy in adulthood had proven unsuccessful (e.g., Hart et al., 1988) and that psychopathic individuals often had antisocial and criminal histories beginning prior to adulthood (Hart & Hare, 1997), Lynam concluded that efforts to interrupt and arrest the development of antisocial and criminal behavior would be aided by the early identification of psychopathic traits in youth. Taken together, these theoretical advancements prompted significant interest in a youth psychopathy construct and in the explicit testing of models of youth psychopathy (e.g., Frick, O'Brien, Wootton, & McBurnett, 1994; Forth, Hart, & Hare, 1990). As a result, in the past 15 years, several research groups have independently worked toward (a) adapting and modifying the construct of adult psychopathy within a developmental context, (b) creating age-appropriate measurement tools to parallel measurement in adult populations, and (c) developing and testing models of youth psychopathy in a variety of cross-sectional and longitudinal youth samples (see Kotler & McMahon, 2005; Lynam & Gudonis, 2005, for reviews).
It should be noted that there remain significant concerns about whether the concept of psychopathy should be applied to youth (e.g., Hart, Watt, & Vincent; 2002; Seagrave & Grisso, 2002; Skeem & Petrila, 2004). Some of the debate surrounding this issue includes (a) conflict about whether delineating psychopathic traits in youth is developmentally appropriate given the malleability of personality during development and the heterogeneity of antisocial youth, (b) questions about the stability of psychopathic traits from youth to adulthood, and (c) concerns about the psychopathy label and its use in legal settings.
As noted above, researchers have made an effort to more accurately assess dimensions of youth psychopathy. Child and adolescent psychopathy measures have been developed by either directly adapting adult assessment tools (primarily the Psychopathy Checklist—Revised [PCL-R]; Hare, 1991, 2003) or creating new screening measures (Forth, Kosson, & Hare, 2003; Frick & Hare, 2001; Lynam, 1997; see Kotler & McMahon, in press, for a review of youth psychopathy assessment methods and issues). The Psychopathy Checklist: Youth Version (PCL:YV; Forth et al., 2003), a direct adaptation of the PCL-R for adolescents, and the Antisocial Process Screening Device (APSD scale; originally called the Psychopathy Screening Device; Frick & Hare, 2001), which includes all elements of the PCL-R unless absolutely not relevant for youth (e.g., multiple marriages), are the tools most commonly used to assess youth psychopathy. However, all of the currently used assessment tools purport to measure a psychopathy construct that is consistent with that described by Hare and colleagues. Furthermore, most of the measures have items/scales that address the affective, interpersonal, and behavioral dimensions of the psychopathy construct. Thus, these youth measures can be viewed as attempting to capture aspects of the psychopathic personality (affective/interpersonal components) as well as the deviant lifestyle and antisocial behaviors that are typically associated with that personality. Moreover, although significant measurement issues continue to be debated, the pattern of relations between the youth psychopathy measures and temperamental and behavioral characteristics suggest that, overall, youth psychopathy assessment tools capture a construct that appears similar to adult psychopathy.
As theory development and research in the domain of juvenile psychopathy have progressed, increasing attention has been paid to the affective/interpersonal component of the psychopathy construct, typically referred to as callous–unemotional (CU) traits in the youth psychopathy literature. In part, this focus on CU traits may have come about as an effort to capture the unique components of the psychopathy construct that are not embedded in established behavioral descriptions of youth antisocial behavior. Furthermore, data suggest that CU traits may be particularly useful in identifying a subgroup of antisocial youth with stable and severe antisocial behavior (Frick & White, 2008) who may differ in their social/emotional, cognitive, and biological functioning (Frick & Viding, 2009). In fact, Frick and colleagues (e.g., Frick, Cornell, Bodin, et al., 2003; Frick & Viding, 2009) proposed that CU traits are the key component of the juvenile psychopathy construct with respect to identifying a unique etiological pathway for early-onset conduct problems. Often, CU traits are operationalized using the CU subscale from the APSD scale (Frick & Hare, 2001). More recently, measurement tools specific to CU traits have also been developed (e.g., interpersonal callousness, Pardini, Obradović, & Loeber, 2006; Inventory of Callous–Unemotional Traits, Frick, 2004).
Using both CU trait-specific approaches and multidimensional youth psychopathy measures, researchers have documented relatively robust and consistent relations (see Frick, 1998; Frick & Marsee, 2006; Lynam & Gudonis, 2005, for reviews) between measures of child and adolescent psychopathy and a range of conduct problems in juvenile offender populations, clinic-referred populations, and community samples (e.g., Christian, Frick, Hill, Tyler, & Frazer, 1997; Dadds, Fraser, Frost, & Hawes, 2005; Forth, 1995; Lynam, 1997, 1998; Salekin, 2008). Taken together, these findings indicate that higher scores on measures of youth psychopathy are positively related to a more severe, pervasive, and stable constellation of conduct problems.
The majority of research on youth psychopathy has utilized concurrent measurements of psychopathy and conduct problems. Although the lack of longitudinal data in this domain is a notable weakness (Moffitt et al., 2008), measures of psychopathy are increasingly being included in longitudinal conduct problem data sets. For example, Loeber and colleagues (2001) assessed psychopathy using the Child Psychopathy Scale (CPS; Lynam, 1996, 1997, 1998) as part of the Pittsburgh Youth Study. The full-length version of the CPS was administered at one time point in the middle cohort of boys (12–13 years of age), while a short version of the CPS (composed of 18 items drawn directly from the Child Behavior Checklist; Achenbach, 1991) was available at all assessment points. Boys with high scores on the CPS were the most frequent, severe, aggressive, and temporally stable delinquent offenders. They were impulsive and prone to externalizing behavior disorders. Moreover, psychopathy predicted serious, stable, antisocial behavior in adolescence above and beyond other known predictors and classification approaches. A recent mixed-model analysis (utilizing the short form of the CPS) indicated that youth psychopathy was relatively stable from childhood through adolescence (i.e., from 7 to 17 years old; intervals examined for stability analyses ranged from 6 months to 5 years) and that both measurement reliability and predictive validity were maintained throughout this lengthy developmental period (Lynam et al., 2009). Lynam, Caspi, Moffitt, Loeber, and Stouthamer-Loeber (2007) also conducted a follow-up assessment of psychopathy in a subsample of the boys from the Pittsburgh Youth Study (n = 271) at the age of 24 using the Psychopathy Checklist: Screening Version (Hart, Cox, & Hare, 1995). These authors reported that psychopathy from early adolescence to early adulthood was moderately stable (r = .31), irrespective of initial risk status or initial psychopathy level and after controlling for 13 other constructs (e.g., demographic information, parenting, and delinquency).
Also using data from the Pittsburgh Youth Study, Pardini and colleagues (2006) constructed a measure of interpersonal callousness and found that higher scores on this measure predicted delinquency persistence in the adolescent cohort. Pardini and Loeber (2008) further identified trajectories of interpersonal callousness over a 4-year period in adolescence and reported that boys with higher initial levels of interpersonal callousness and those with trajectories that increased or did not decline had the highest level of antisocial personality characteristics at age 26.
Recent studies have also examined scores on the PCL:YV (Forth et al., 2003) as a predictor of future recidivism. Schmidt, McKinnon, Chattha, and Brownlee (2006) examined the PCL:YV in a multiethnic community sample of 130 adjudicated male and female adolescents. At a mean follow-up of 3 years, the PCL:YV predicted general and violent recidivism in male Caucasian and Native Canadian youth. Examining a sample of 130 youth involved in court assessments, Salekin (2008) showed that, after controlling for a host of variables relating to offending, PCL:YV scores predicted general and violent recidivism over a 3- to 4-year period from mid-adolescence to young adulthood.
Several studies utilizing community samples have also provided valuable longitudinal outcome data. For example, Frick, Stickle, Dandreaux, Farrell, and Kimonis (2005) followed a sample of 98 youth (Grades 4–7 at baseline) for 4 years. They found that youth with both baseline conduct problems and CU traits subsequently demonstrated the highest rates of conduct problems, self-reported delinquency, and police contacts. Compared to youth without initial conduct problems, youth with baseline conduct problems who did not evidence CU traits also showed higher rates of conduct problems, but rates of self-reported delinquency were not elevated. Piatigorsky and Hinshaw (2004) constructed a psychopathy prototype using items from the California Child Q-Set and found that children with a high degree of similarity to the prototype had more severe delinquency at a 5- to 7-year prospective follow-up, even after controlling for baseline conduct problems. Similarly, Dadds and colleagues (2005) found that, after accounting for initial antisocial behavior, CU traits predicted antisocial behavior for boys (ages 4–9 years) and older girls (ages 7–9 years) at a 12-month follow-up. Examining a very large community sample in Great Britain (n = 7,636 youth ages 5–16 years), Moran et al. (2009) found that CU traits were positively associated with psychopathology at a 3-year follow-up.
Overall, the currently available longitudinal data suggest that measures of youth psychopathy account for significant variation in later conduct problem outcomes and even adult antisocial behavior. However, it is notable that the magnitude of these relations has varied widely across studies and tends to be larger in offender populations.
In the context of this limited but growing body of longitudinal findings, there has been significant concern about overlap between youth psychopathy (both the multidimensional construct and the CU traits component) and conduct problem constructs, especially when psychopathy is measured in nonoffender populations where baseline levels of conduct problems vary widely (e.g., Burns, 2000; Dadds et al., 2005). In particular, it is possible that many relations between youth psychopathy and subsequent conduct problems are due to significant shared variance between measures of psychopathy and other established measures of conduct problem severity (e.g., initial severity of conduct problems, timing of conduct problem onset). Consequently, some researchers have questioned whether psychopathy constructs can provide added value to existing conduct problem models and current Diagnostic and Statistical Manual of Mental Disorders (4th ed. [DSM–IV]; American Psychiatric Association, 1994) and proposed DSM–V CD and subtyping criteria (e.g., Burns, 2000; Moffitt et al., 2008). To provide an accurate response to this question, dimensions of youth psychopathy must be evaluated in the context of other commonly used predictors of conduct problem outcomes (Burns, 2000; Dadds et al., 2005; Frick, 2000). As noted in the review of extant longitudinal data, several authors have begun to address this issue. For example, Dadds et al. (2005), Moran et al. (2009), and Piatigorsky and Hinshaw (2004) found that psychopathy measures predicted significant variance in conduct problem behavior after controlling for baseline conduct problems. However, not all studies have yielded this pattern of results. Salekin, Neumann, Leistico, DiCicco, and Duros (2004) found that, although the PCL:YV (Forth et al., 2003) predicted previous offenses above and beyond oppositional defiant disorder (ODD) and CD diagnoses, the APSD scale (Frick & Hare, 2001) did not do so.
In addition, whether CU traits contribute incremental utility over information provided by comorbid attention-deficit/hyperactivity disorder (ADHD) has not been well established (Frick & Moffitt, 2010). ADHD is the comorbid condition most commonly associated with conduct problems and is thought to precede the development of conduct problems in the majority of cases. In fact, some investigators consider ADHD (or, more specifically, the impulsivity or hyperactivity components of ADHD) to be the motor that drives the development of early-onset conduct problems, especially for boys (e.g., Burns & Walsh, 2002; Loeber, Farrington, Stouthamer-Loeber, & Van Kammen. 1998). Coexisting ADHD also predicts a more negative life outcome than do conduct problems alone (see Waschbusch, 2002).
The current study was designed to specifically address the issue of whether the youth psychopathy construct provides added value to existing models of conduct problems, including the diagnostic subtyping criteria of CD in the DSM–IV and the presence of comorbid ADHD. We focused our investigation on CU traits because this affective/interpersonal component of the psychopathy construct can be more clearly differentiated from behavioral definitions of conduct problems and because CU traits are under consideration as a specifier for CD in the DSM–V (Frick & Moffitt, 2010). In particular, we examined the predictive validity of CU traits measured in early adolescence (Grade 7) for subsequent antisocial outcomes and early adult antisocial personality disorder characteristics in the context of existing predictors of conduct problem severity. Three primary research questions were evaluated: (a) Do CU traits predict later antisocial outcomes above and beyond existing measures of childhood conduct problems and ADHD? (b) How accurately do CU traits identify individuals who engage in antisocial behavior in young adulthood compared to other established predictors of antisocial behavior, and does a CU trait specifier (as proposed for DSM–V) add predictive value to an existing CD diagnosis? (c) Does the predictive validity of CU traits vary as a function of youths' sex, race, or urban/rural status?
To address these questions, CU traits were measured in Grade 7 using the CU traits subscale of the parent-report version of the APSD scale (Frick & Hare, 2001). Antisocial outcomes, measured in adolescence and early adulthood, included (a) self-reported delinquency from seventh grade through 2 years post–high school; (b) self-reported serious crimes through 2 years post–high school, as well as both juvenile and adult arrest records through 1 year post–high school; and (c) antisocial personality disorder symptoms and diagnosis at 2 years post–high school. We controlled for earlier measures of conduct problems (e.g., ODD and CD criterion counts, childhood onset of CD) and ADHD (criterion count). Finally, there is a significant shortage of research on girls and ethnic minority youth who exhibit CU traits (Moffitt et al., 2008); furthermore, to our knowledge, urban versus rural status of these youth also has not been investigated. Thus, sex, race, and urban/rural status were explored as potential moderators.
Method Participants
Participants came from the control schools of a longitudinal multisite investigation of the development and prevention of childhood conduct problems, the Fast Track project (Conduct Problems Prevention Research Group, 1992, 2000). Schools within four sites (Durham, North Carolina; Nashville, Tennessee; Seattle, Washington; and rural Pennsylvania) were identified as high risk based on crime and poverty statistics of the neighborhoods that they served. Within each site, schools were divided into sets matched for demographics (size, percentage free or reduced lunch, ethnic composition), and the sets were randomly assigned to control and intervention groups. Using a multiple-gating screening procedure that combined teacher and parent ratings of disruptive behavior, 9,594 kindergarteners across three cohorts (1991–1993) from 55 schools were screened initially for classroom conduct problems by teachers, using the Teacher Observation of Child Adjustment—Revised Authority Acceptance score (Werthamer-Larsson, Kellam, & Wheeler, 1991). Those children scoring in the top 40% within cohort and site were then solicited for the next stage of screening for home behavior problems by the parents, using items from the Child Behavior Checklist (Achenbach, 199l) and similar scales, and 91% agreed (n = 3,274). The teacher and parent screening scores were then standardized and summed to yield a total severity-of-risk screen score. Children were selected for inclusion into the high-risk sample based on this screen score, moving from the highest score downward until desired sample sizes were reached within sites, cohorts, and groups. Deviations were made when a child failed to matriculate in the first grade at a core school (n = 59) or refused to participate (n = 75) or to accommodate a rule that no child would be the only girl in an intervention group. The outcome was that 891 children (control = 446, intervention = 445) participated. In addition to the high-risk sample of 891, a stratified normative sample of 387 children was identified to represent the population normative range of risk scores and was followed over time. From among the control schools (n = 27), teachers completed ratings of child disruptive behavior to identify a normative, within-site stratified sample of about 10 children within each decile of behavior problems.
The current study utilized data from the high-risk control group (65% male; 49% African American, 48% European American, 3% other race) and normative sample (51% male; 43% African American, 52% European American, 5% other race). Because 79 of those recruited for the high-risk control group were also included as part of the normative sample, the final sample for the current analyses included 754 participants. Weighting was used in all analyses to reflect the oversampling of high-risk children. Participants from the high-risk intervention sample were not included in this study.
Measures
Antisocial Process Screening Device (APSD)
Youth psychopathy was assessed in the summer after seventh grade using the parent version of the APSD scale (Frick & Hare, 2001). Scoring on this 20-item rating scale of youth behaviors is based on a 3-point scale: 0 (not at all true), 1 (sometimes true), or 2 (definitely true). The APSD scale has been shown to have adequate test–retest reliability (Christian et al., 1997). We used the APSD scale three-factor structure identified by Frick, Bodin, and Barry (2000) that includes CU traits, narcissism, and impulse control/conduct problems factors. A confirmatory factor analysis indicated that this factor structure adequately fit the APSD scale data from participants in the current study (Kotler, McMahon, & the Conduct Problems Prevention Research Group, 2002; comparative fit index = .91; goodness-of-fit index = .92). Only the CU factor score was employed in the present investigation. Similar to findings reported by Frick et al. in their examination of the APSD scale in a large community sample, CU scores in our normative sample were moderately skewed (skewness = 0.241). In contrast, CU scores in our high-risk sample were not significantly skewed (skewness = −0.004). This finding is consistent with results from other studies utilizing the APSD scale with high-risk populations (e.g., Pardini, Lochman, & Powell, 2007). In addition, a CU trait specifier was calculated for use in the sensitivity analyses. This specifier was developed using the criteria described by Frick and Moffitt (2010) and proposed for DSM–V. The Frick and Moffitt criteria include a CD diagnosis as well as the presence of two or more of the following CU traits for at least 12 months and in more than one setting: (a) lack of remorse or guilt, (b) callous–lack of empathy, (c) unconcerned about performance, and (d) shallow or deficient affect. In the current study, four items from the CU factor of the APSD scale that correspond to the four traits described by Frick and Moffitt were used to create the CU specifier: (a) does not feel guilty, (b) unconcerned about the feelings of others, (c) unconcerned about school/work, and (d) does not show emotion. For the purposes of the current study, we calculated a CU trait cutoff, defined as having a score of 2 (definitely true) on at least two of the four items from the APSD CU traits scale. This cutoff, in combination with a CD diagnosis, was utilized as the CU trait specifier in the sensitivity analysis.
Self-Report of Delinquency
The Self-Report of Delinquency (SRD; Elliott, Huizinga, & Ageton, 1985) measure was administered from Grades 7 through 12, as well as the 2 years following high school, and captured the number of times in the past year the respondent committed 34 different offenses. Offenses range from lying about one's age to get something to attacking someone with the intent to hurt. Following earlier use of the measure (e.g., Elliott et al., 1985), the items in each grade were capped at three to avoid creating an extremely skewed distribution. The SRD general delinquency outcome measure was defined as the mean of all 34 items within each year, with a possible range of 0–1. A count measure of serious crimes in the 2 years following high school was created by summing the number of 13 items from the SRD that represent serious offenses, including stealing, physical violence toward others, and selling drugs. Thus, the possible range for the serious crimes variable was 0–13.
Court records
Juvenile and adult arrest information was collected from the court system in the child's county of residence and surrounding counties through 1 year post–high school. A court record of arrest indicates any crime for which that youth was arrested and adjudicated, with the exception of probation violations (which were inconsistently reported in courts across the four sites) and referrals to youth court diversion programs for very young first-time offenders (starting at age 11). Other offenses leading to youth diversion programs were included as long as there was an identified arrest in the records.
The data collected from the courts included a description of the offense, the date of offense, the adjudication date for the arrest, and the outcome of the arrest. To capture both frequency and severity of the crimes for which youth were arrested, we created a lifetime severity-weighted frequency of juvenile and adult arrests (Cernkovich & Giordano, 2001). Each offense for each arrest was assigned a severity score ranging from 1 to 5. Level 5 included all violent crimes, such as murder, rape, kidnapping, and first-degree arson. Level 4 contained crimes involving serious or potentially serious harm and included assault with weapon and first-degree burglary. Level 3 crimes reflected medium severity, such as simple assault, felonious breaking and entering, possession of controlled substances with intent to sell, and fire setting. Level 2 included low-severity crimes such as breaking and entering, disorderly conduct, possession of controlled substance, shoplifting, vandalism, and public intoxication. Level 1 involved status and traffic offenses. We then summed the severity level of the most severe offense from each arrest from Grade 6 through 1 year post–high school (separately for adult and juvenile arrests).
Psychiatric criterion counts and disorders
The Parent Interview version of the NIMH Diagnostic Interview Schedule for Children (DISC) is a well-validated, highly structured, laptop computer–administered, clinical interview to assess DSM–IV symptoms in children and adolescents ages 6 to 17 years. We used Version 2.3 in Grade 3 (and the published anticipated DSM–IV criteria for diagnosis at that time) and Version IV in Grades 6, 9, and 12 (Shaffer & Fisher, 1997; Shaffer et al., 1996; Shaffer, Fisher, Lucas, & Comer, 2003). Lay interviewers, unaware of control/normative status, were trained until they reached reliability. Administration took place in the child's home with the primary parent, usually the mother, during the summer following Grades 3, 6, 9, and 12. Variables were computed for past-year criterion counts and diagnoses for CD, ODD, and ADHD. Criteria were solicited for the past 6 months for ODD and for the past 12 months for CD and ADHD. CD scores were based on 15 criteria derived from 23 symptom items, with actual scores ranging from 0 to 9. ODD scores were based on eight criteria derived from 12 symptom items, with scores ranging from 0 to 8. ADHD scores were based on 18 criteria derived from 21 symptom items, with scores ranging from 0 to 18. Diagnoses for Grade 3 followed from Diagnostic and Statistical Manual of Mental Disorders (3rd ed., rev.; American Psychiatric Association, 1987) criteria, and diagnoses for Grades 6, 9, and 12 followed from DSM–IV criteria.
The DISC–Young Adult version (DISC-YA; Shaffer, Fisher, Lucas, Dulcan, & Schwab-Stone, 2000) was administered to the youth at 2 years post–high school. Antisocial personality disorder diagnosis was based on having three or more criteria derived from seven symptom items, with actual scores ranging from 0 to 7 (M = 0.95, SD = 1.45).
Analysis Plan
To address the primary research questions described earlier, we conducted three sets of analyses to examine the relation between CU traits measured in Grade 7 using the APSD scale and six antisocial outcome measures: self-reported delinquency averaged across Grade 7 through 2 years post–high school, self-reported serious crimes in the 2 years following high school, severity-weighted juvenile and adult arrests, and antisocial personality disorder criterion count and diagnoses 2 years following high school. In the first set of analyses, we estimated the relation between CU traits and antisocial outcomes, while controlling for measures of conduct problems (i.e., CD and ODD criterion counts or diagnosis, childhood onset of CD, assessed in Grades 3, 6, 9, and 12) and ADHD (criterion score or diagnosis, assessed in Grades 3, 6, 9, and 12). For continuous outcomes (self-reported delinquency, serious crimes, juvenile and adult arrests, and antisocial personality disorder criterion count), the criterion counts of CD, ODD, and ADHD were included as covariates. For the binary outcome (antisocial personality disorder diagnosis), CD, ODD, and ADHD diagnoses were included as covariates.
In the second set of analyses, we examined the predictive accuracy of CU traits and other measures of conduct problems for successfully identifying individuals who engaged in antisocial behavior in young adulthood. In the third set of analyses, we incorporated three demographic measures to determine whether the predictive validity of CU traits applied equally to all individuals, regardless of sex, race, or urban/rural status.
All of the continuous antisocial outcome measures were count measures with significant positive skew (skewness ranged from 1.73 for antisocial personality disorder criterion count to 6.10 for self-reported serious crimes post–high school). To accommodate the distributions, we used a negative binomial regression model (Hilbe, 2007), which is an extension of the Poisson model that allows for overdispersion (when the variance of the outcome is greater than the mean of the distribution). For the dichotomous outcome—antisocial personality disorder diagnosis—a logistic regression model was used. The second set of analyses was a binary classification test to determine the sensitivity, specificity, and predictive value of the various measures of conduct problems and ADHD in the prediction of antisocial outcomes (Altman & Bland, 1994). Sensitivity represents the proportion of individuals who exhibited antisocial outcomes, given the predictor (i.e., CU traits) was present. One minus the sensitivity provides the false-negative rate, or the rate of missing the prediction of antisocial outcomes. Specificity is the proportion of individuals who did not exhibit antisocial outcomes, given the predictor was absent. One minus the specificity provides the false-positive rate, which is the proportion of individuals who were inaccurately predicted to exhibit antisocial outcomes. The positive predictive value of an indicator was calculated as the proportion of individuals exhibiting antisocial outcomes who were predicted to exhibit antisocial outcomes. Negative predictive value was the proportion of individuals not exhibiting antisocial outcomes who were not predicted to exhibit antisocial outcomes. Ideally, both sensitivity and specificity will be high, which would indicate that the predictor correctly identifies those who will develop antisocial outcomes and correctly identifies those who will not develop antisocial outcomes.
For the third set of analyses, we conducted moderated regression analyses (Aiken & West, 1991) to examine whether sex, race, and urban/rural status moderated the relation between CU traits and antisocial outcomes. The interaction between CU traits and the potential moderators (sex, race, and urban/rural status) was calculated by mean-centering the CU traits score and multiplying the centered CU traits scale score by dummy-coded moderators. The regression models described above for the first set of analyses were repeated with the main effects of CU traits, the main effects of each moderator, and the interaction between CU traits and each moderator. Given the large number of tests, we used a corrected alpha of p < .01.
Regression models were conducted with Mplus Version 5.2 (Muthén & Muthén, 2007). Missing outcome data were accommodated using full-information maximum likelihood (ML) with robust standard errors and numerical integration, which provides an estimate of the variance–covariance matrix using all of the available information from the observed data (Schafer, 1997; Schafer & Graham, 2002). ML assumes data are missing at random (MAR), which means the function by which data are missing can be characterized (probabilistically) by the observed data. By controlling for observed variables that predict the missingness function, the conditional likelihood of the missing value becomes independent of the outcome of interest (Rubin, 1976). The most missing data occurred on the DISC-YA measure, with 33% (n = 250) missing at 2 years post–high school. Attrition analyses indicated that race, urban/rural status, and CU trait scores were significantly associated with whether data were missing; therefore, data were assumed to be MAR with these variables included in all models. Our effective sample sizes were 754 for the SRD and arrests models and 504 for the antisocial personality disorder criterion counts/diagnosis models.
ResultsDescriptive information (i.e., means and standard deviations of continuous measures) and the bivariate correlations for all measured variables are provided in Table 1. As shown with boldfacing, many of the predictor variables were significantly correlated (p < .01) with the six primary outcome variables. There were also significant correlations within each scale (e.g., DISC, SRD). Overall, self-report of general delinquency from Grade 7 to 2 years post–high school was most highly correlated with CD criterion count. Self-report of serious crimes during the 2 years post–high school was most highly correlated with CD and ADHD criterion counts. Adult arrests were most highly correlated with CD criterion count and CU traits. Juvenile arrests were most highly correlated with ADHD and CD criterion counts. Antisocial personality disorder criterion count and diagnosis were most strongly correlated with CD criterion count and child onset of CD. Table 1 also provides the frequencies (percentage of sample endorsing) of antisocial personality disorder diagnosis as well as child onset of CD.
Bivariate Correlations, Means, and Standard Deviations for All Measured Variables
Predictive Validity of CU Traits
The first set of analyses evaluated whether CU traits predicted additional variance in later antisocial outcomes over existing measures of childhood conduct problems and ADHD. As seen in Table 2, the CU traits subscale of the APSD was significantly associated with average SRD scores (i.e., general delinquency), juvenile and adult arrests, and both antisocial personality disorder criterion count and diagnosis. The direction of the effects was such that higher levels of CU traits predicted higher levels of self-reported general delinquency, more juvenile and adult arrests, greater number of antisocial personality disorder criteria met, and a higher likelihood of antisocial personality disorder diagnosis. CD criterion count significantly predicted self-reported general delinquency scores and juvenile and adult arrests. ODD criterion count and ADHD criterion count significantly predicted self-reported serious crimes, and child onset of CD predicted antisocial personality disorder criterion count and diagnosis.
Standardized Regression Coefficients for CU Traits Predicting Outcomes, Including CD, ODD, ADHD, and Child-Onset Criteria as Covariates
Positive Predictive Value and Specificity of CU Traits
The level of the predictive accuracy of the CU traits scale of the APSD, in comparison to other predictors of antisocial outcomes, was evaluated by calculating the sensitivity, specificity, positive predictive value, and negative predictive value of each predictor (Altman & Bland, 1994). A total antisocial index, defined by one or more antisocial outcomes (i.e., the presence of any severity-weighted juvenile or adult arrests, at least one serious crime, or a diagnosis of antisocial personality disorder), was used as the outcome in the sensitivity/specificity analyses. Forty-seven percent of the sample (n = 356) qualified for at least one antisocial outcome.
Table 3 provides each value. ODD diagnosis had the highest level of sensitivity (.43), whereas CD diagnosis with the CU traits cutoff score had the highest specificity (.99). The CD diagnosis with the CU traits cutoff score also had the highest positive predictive value (.89). Therefore, incorporating the CU traits specifier for those with a diagnosis of CD improves positive prediction of antisocial outcomes, with a very low false-positive rate (.01). In the current sample, only one of the nine individuals who were diagnosed with CD and exhibited CU traits did not also exhibit later antisocial outcomes. Negative predictive values and sensitivity were relatively low across the conduct problem and ADHD predictors because of the large number of individuals with antisocial outcomes.
Sensitivity and Specificity of Antisocial Outcomes for Each Conduct Problem/ADHD Predictor
Consistency of Effects Across Sex, Race, and Urban/Rural Status
In the final set of analyses, sex, race, and urban/rural status were examined as moderators of the relation between CU traits and each antisocial outcome using moderated regression analyses. CU traits scale scores were centered and multiplied by the dichotomized sex, race, and urban/rural status variables to create separate interaction terms (Aiken & West, 1991). The only significant interaction effect was an interaction between CU traits and urban/rural status in the prediction of adult arrests (β = −0.84, p < .001). Probing the interaction using simple slopes indicated that the association between CU traits and adult arrests was significantly greater among individuals from urban areas (r = .26, p < .001) as compared to rural areas (r = .11, p = .17).
DiscussionThis study focused on the predictive validity of CU traits, measured in early adolescence, with respect to multiple antisocial outcomes in adolescence and young adulthood. We employed a longitudinal sample with 15 years of annual data collection beginning in kindergarten and extending through 2 years post–high school. Multiple antisocial outcomes were measured, including general delinquent behavior from seventh grade through 2 years post–high school (approximately age 20) and serious crimes in the 2 years following high school, both derived from youth self-report; juvenile and adult arrests through 1 year post–high school, as measured by both youth self-report and court records; and antisocial personality disorder criterion count and diagnosis, as measured by youth self-report.
Three primary research questions were addressed using analytic models designed to focus on assumptions regarding the underlying distribution of the data in the population: (a) Do CU traits predict later antisocial outcomes above and beyond existing measures of childhood conduct problems and ADHD? (b) How accurately do CU traits identify individuals who engage in antisocial behavior in young adulthood compared to other established predictors of antisocial behavior, and does a CU trait specifier (as proposed for DSM–V) add predictive value to an existing CD diagnosis? (c) Does the predictive validity of CU traits vary as a function of youths' sex, race, or urban/rural status? Our findings with regard to each of these questions are discussed below.
Does the CU Traits Construct Provide Added Value to Existing Models of Conduct Problems?
Overall, the results indicated that the measure of CU traits administered to parents in seventh grade (i.e., from the APSD scale) was highly predictive of five of the six antisocial outcomes: self-reported general delinquency, juvenile and adult arrests, and both early adult antisocial personality disorder criterion count and diagnosis. Of import, however, was whether information about CU traits provided incremental value in terms of predictive validity over other well-established predictors of antisocial outcomes, such as criterion counts of ODD and CD, childhood-onset status of CD, and ADHD criterion count, assessed from Grade 3 to Grade 12. Surprisingly, the measure of CU traits was more predictive of later antisocial outcomes than any of these other predictors. This was the case for general delinquency, juvenile and adult arrests, and antisocial personality disorder criterion count and diagnosis.
The current findings add to the existing literature in several key ways. First, there have been conflicting results regarding the added predictive value of CU traits and psychopathy data taken from psychopathy screening measures above and beyond frequently used predictors of antisocial behavior (e.g., baseline conduct problems, ODD and CD diagnoses). While several research groups have found that psychopathy measures predict significant variance in conduct problem behavior after controlling for baseline conduct problems (e.g., Dadds et al., 2005; Moran et al., 2009; Piatigorsky & Hinshaw, 2004), Salekin et al. (2004) looked specifically at the predictive validity of the APSD scale above and beyond ODD and CD diagnoses and did not find a significant effect. Thus, our results lend further weight to the contention that data from CU traits and psychopathy screening measures can provide added predictive validity in the context of a rigorous analytic design including multiple often-used predictors of antisocial behavior. Second, in the extant CU traits and psychopathy literature, insufficient attention has been paid to ADHD as a primary predictor of antisocial behavior (Frick & Moffitt, 2010). Thus, our findings demonstrating the incremental predictive validity of CU traits above and beyond an ADHD measure also augment the current knowledge base in this regard. Finally, results from the current study move beyond demonstrating that CU traits provide incremental predictive validity, in that, with respect to a number of key antisocial outcomes, CU traits were shown to be a more salient predictor than other frequently used conduct problem measures. Establishing CU traits as a key predictor of antisocial outcomes is of primary importance when considering the addition of a CU traits specifier to the diagnosis of CD in DSM–V.
It is important to note that only 5% of participants in the current sample met the criteria for CU traits described by Frick and Moffitt (2010), suggesting that children who meet the CU traits criteria are at extremely high risk for engaging in antisocial acts. Considering that recent prevalence estimates of antisocial personality disorder in the general population are only 3.6% (Grant et al., 2004) and the high degree of specificity for CU traits in predicting antisocial outcomes, it is not surprising that so few participants in the current sample reported CU traits. Nonetheless, the results from the current study should be interpreted with some caution until replicated in a larger sample.
How Accurately Do CU Traits Identify Individuals Who Engage in Antisocial Behavior in Young Adulthood Compared to Other Established Predictors of Antisocial Behavior, and Does a CU Traits Specifier (as Proposed for DSM–V) Add Predictive Value to an Existing CD Diagnosis?
To examine the predictive accuracy of the CU traits scale, in comparison to other predictors of antisocial outcomes, we calculated the sensitivity, specificity, positive predictive value, and negative predictive value of each predictor with respect to a total antisocial index (Altman & Bland, 1994). This index was defined by one or more of four antisocial outcomes (i.e., the presence of any severity-weighted juvenile or adult arrests, at least one serious crime, or a diagnosis of antisocial personality disorder). Almost half of the sample displayed at least one antisocial outcome, providing a wide range of individuals who engaged in antisocial behavior. However, the high rate of antisocial outcomes resulted in low sensitivity (below .43) across all predictors, primarily because of the high number of false negatives (meaning youth who engaged in antisocial behavior but did not meet diagnostic criteria).
Incorporating the CU traits specifier for those with a diagnosis of CD improved positive prediction of antisocial outcomes, with a very low false-positive rate (.01) and with the highest positive predictive value (.89). Only one of the nine individuals who were diagnosed with CD and exhibited CU traits did not also exhibit later antisocial outcomes. As noted previously, several recent studies have evaluated the predictive validity of CU traits and the psychopathy construct in the context of other predictors of antisocial outcomes (e.g., Dadds et al., 2005; Piatigorsky & Hinshaw, 2004; Salekin, 2008). However, to our knowledge, this is the first investigation to specifically address the predictive accuracy of CU traits in relation to other commonly used predictors of antisocial behavior (cf. Frick & Moffitt, 2010). When combined with the findings from our first analysis showing that, compared to other commonly used measures, CU traits provide superior prediction with respect to a number of antisocial outcomes, results demonstrating that the inclusion of CU traits data improves predictive accuracy lend additional weight to the assertion that a CU traits specifier for the diagnosis of CD would be a valuable addition to the diagnostic framework.
It is also important to note that child onset of CD, which is currently a subtype of CD in the DSM–IV, also had a low false-positive rate (.04) and good positive predictive value (.82). These findings provide support for the current proposal to retain the age-of-onset distinction in the DSM–V (Frick & Moffitt, 2010).
Does the Predictive Validity of CU Traits Vary as a Function of Youths' Sex, Race, or Urban/Rural Status?
As noted above, there has been a paucity of research concerning whether or not various demographic variables might serve to moderate the predictive validity of CU traits on antisocial outcomes. To our knowledge, this is the first study to examine sex, race (African American vs. non–African American), and urban/rural status in the same sample.
There was minimal moderation of the effects of CU traits by sex, race, or urban/rural status. The relation between CU traits and adult arrests was somewhat stronger for urban participants than it was for rural participants. This interaction can be partially explained by the significantly higher rate of adult arrests among youth from urban areas and the correspondingly significantly higher scores on the CU traits scale among African Americans from urban areas. To our knowledge, this is the first study to examine urban/rural status as a potential moderator of the effects of CU traits on later antisocial outcomes. The failure of these demographic variables to moderate the predictive relationship between CU traits and nearly all antisocial outcomes (measured up to 7 years later) underscores the robustness of the link between CU traits and antisocial outcomes. However, it is important to note that detecting significant interaction effects can be extremely difficult, particularly when variable distributions are skewed (McClelland & Judd, 1993). Future research should continue to examine whether demographic characteristics may moderate the association between CU traits and antisocial outcomes.
Implications for DSM–V
Findings clearly support the inclusion of presence of CU traits as a possible specifier for the diagnosis of CD (Frick & Moffitt, 2010), at least with respect to predictive validity. Higher levels of CU traits (measured in seventh grade) were associated with a more negative prognosis on five of six antisocial outcomes employed in this study, including self-reported general delinquency, juvenile and adult arrests, and antisocial personality disorder criterion count and diagnosis. Of even greater significance, our indicator of CU traits provided incremental value in terms of predictive validity over other well-established predictors of antisocial outcomes, including previous and current criterion counts of ODD, CD, and ADHD and childhood-onset status of CD. The superior performance of CU traits in predicting later antisocial outcomes strongly suggests that they may have a place in the diagnostic system for CD in the forthcoming DSM–V, along with retention of the age-of-onset subtyping distinction currently in place.
Finally, the findings supported the general robustness of the relation between CU traits and later antisocial outcomes. This was the case during adolescence, with no evidence of moderation by sex, race, or urban/rural status found for either general delinquency or juvenile arrests, as well as early adulthood, with no evidence of moderation for serious crimes or antisocial personality disorder criterion count and diagnosis. The only evidence of moderation was that the connection between CU traits and adult arrests was stronger for urban participants than for rural participants. Future research should be conducted to examine the interaction between living in urban areas and CU traits in the prediction of adult arrests.
Overall, these findings are supportive of serious consideration of the inclusion of CU traits as a specifier for the diagnosis of CD in the upcoming DSM–V.
Footnotes 1 Although less explicit in nature, previous attempts have been made to extend the psychopathy construct to youth populations. Notably, the Diagnostic and Statistical Manual of Mental Disorders (3rd ed. [DSM–III]; American Psychiatric Association, 1980) differentiated children with conduct disorder (CD) who were socialized or undersocialized. The undersocialized type was connected to traditional views of the adult psychopathic personality (primarily the interpersonal/affective factor), while the socialized type of CD focused more on an environmental/behavioral etiology of conduct problems. Within this system, youth were also categorized as aggressive/nonaggressive. See Frick and Ellis (1999) for a detailed discussion of this DSM–III subtyping approach and its association with the youth psychopathy construct.
2 Research findings indicating that the youth psychopathy construct actually seems fairly stable across multiple-year intervals (e.g., Frick, Cornell, Barry, Bodin, & Dane, 2003; Lynam et al., 2009) suggest that this concern may be less relevant than initially thought.
3 It is notable that Murrie, Boccaccini, McCoy, and Cornell (2007) recently found that while behavioral history and personality descriptions influenced judges' decisions, the psychopathy label itself did not.
4 Validity studies for the DISC-YA, including the antisocial personality disorder module, have not been conducted (P. Fisher, personal communication, March 24, 2010). However, support for the construct validity of the DISC-YA antisocial personality disorder criterion count and diagnosis comes from their positive and statistically significant associations with measures of self-reported general delinquency and serious crimes and with juvenile and adult arrests in the current sample (coefficients ranging from .25 to .39, all ps < .01).
5 Initially, latent growth models were estimated for the SRD scores from Grade 7 through Grade 12; however, the results indicated nonsignificant change in SRD over time and a main effect for mean level of SRD. Thus, to simplify the results, we report the relations between conduct problems, CU traits, and mean SRD over time.
6 There is no way of determining whether the MAR assumption holds in any one data set. Fortunately, Collins, Schafer, and Kam (2001) showed that inaccurately assuming MAR, when data are missing not at random, has a minor impact on the ML estimates and standard errors.
7 In the context of the regression analyses, the regression coefficient for ODD predicting self-reported serious crimes was in the opposite direction to the bivariate correlation, indicating a suppression effect (J. Cohen, Cohen, West, & Aiken, 2003) due to high intercorrelations between CD, ODD, and ADHD criterion counts and the stronger associations between serious crimes and both CD and ADHD criterion counts.
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Submitted: July 13, 2009 Revised: April 13, 2010 Accepted: April 15, 2010
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Source: Journal of Abnormal Psychology. Vol. 119. (4), Nov, 2010 pp. 752-763)
Accession Number: 2010-21298-001
Digital Object Identifier: 10.1037/a0020796
Record: 42- Title:
- Prognostic significance of spouse we talk in couples coping with heart failure.
- Authors:
- Rohrbaugh, Michael J.. Department of Psychology, University of Arizona, Tucson, AZ, US, michaelr@u.arizona.edu
Mehl, Matthias R.. Department of Psychology, University of Arizona, Tucson, AZ, US
Shoham, Varda. Department of Psychology, University of Arizona, Tucson, AZ, US
Reilly, Elizabeth S.. Department of Psychology, University of Arizona, Tucson, AZ, US
Ewy, Gordon A.. Department of Cardiology, University of Arizona, Tucson, AZ, US - Address:
- Rohrbaugh, Michael J., Department of Psychology, University of Arizona, P.O. Box 210068, Tucson, AZ, US, 85721, michaelr@u.arizona.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 76(5), Oct, 2008. pp. 781-789.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 9
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- marriage, close relationships, heart disease, coping, text analysis, marital quality
- Abstract:
- Recent research suggests that marital quality predicts the survival of patients with heart failure (HF), and it is hypothesized that a communal orientation to coping marked by first-person plural pronoun use (we talk) may be a factor in this. During a home interview, 57 HF patients (46 men and 16 women) and their spouses discussed how they coped with the patients' health problems. Analysis of pronoun counts from both partners revealed that we talk by the spouse, but not the patient, independently predicted positive change in the patient's HF symptoms and general health over the next 6 months and did so better than direct self-report measures of marital quality and the communal coping construct. We talk by the patient and spouse did not correlate, however, and gender had no apparent moderating effects on how pronoun use predicted health change. The results highlight the utility of automatic text analysis in couple-interaction research and provide further evidence that looking beyond the patient can improve prediction of health outcomes. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Coping Behavior; *Heart Disorders; *Marital Satisfaction; *Marriage; *Interpersonal Relationships; Spouses
- Medical Subject Headings (MeSH):
- Adaptation, Psychological; Aged; Communication; Female; Heart Failure; Humans; Male; Marriage; Middle Aged; Prognosis; Quality of Life; Semantics; Sick Role; Spouses; Ventricular Dysfunction, Left; Verbal Behavior
- PsycINFO Classification:
- Cardiovascular Disorders (3295)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Constructive Communication Scale
Hopkins Symptom Checklist DOI: 10.1037/t06011-000
Relationship Assessment Scale DOI: 10.1037/t00437-000 - Grant Sponsorship:
- Sponsor: American Heart Association, US
Grant Number: 0051286Z
Other Details: Award
Recipients: No recipient indicated - Methodology:
- Empirical Study; Qualitative Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Jun 23, 2008; Revised: Apr 7, 2008; First Submitted: Oct 22, 2007
- Release Date:
- 20081006
- Correction Date:
- 20140811
- Copyright:
- American Psychological Association. 2008
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0013238
- PMID:
- 18837595
- Accession Number:
- 2008-13625-007
- Number of Citations in Source:
- 53
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2008-13625-007&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2008-13625-007&site=ehost-live">Prognostic significance of spouse we talk in couples coping with heart failure.</A>
- Database:
- PsycINFO
Prognostic Significance of Spouse We
Talk in Couples Coping With Heart Failure
By: Michael J. Rohrbaugh
Department of Psychology, University of Arizona;
Department of Family Studies/Human Development,
University of Arizona;
Matthias R. Mehl
Department of Psychology, University of Arizona
Varda Shoham
Department of Psychology, University of Arizona
Elizabeth S. Reilly
Department of Psychology, University of Arizona
Gordon A. Ewy
Department of Cardiology, University of Arizona;
Sarver Heart Center,
University of Arizona
Acknowledgement: Elizabeth S. Reilly is now with the
British National Health Service, Glasgow, Scotland.
This research was supported by Award
0051286Z from the American Heart Association, Dallas, Texas.
We thank Christopher Wenner for his help in administering
the Arizona Family Heart Project; Mary Brown, Lisa
Hoffman-Konn, Mary-Frances O'Conner, Josh Schoenfeld, and
Sarah Trost for conducting the interviews; Paul Fenster,
Lorraine Macstaller, Brendan Phipps, Edna Silva, Marti
Simpson, and Lawton Snyder for their help in recruiting
participants; and Jeffrey Berman for suggestions about
statistical analysis.
A growing body of research highlights the key role of close
relationships in successful coping with heart disease and other
forms of chronic illness. For example, in a study of 189
heart-failure (HF) patients and their spouses from Michigan, a
composite measure of marital quality predicted the patient's
survival over the next 8 years independent of baseline illness
severity (Rohrbaugh,
Shoham, & Coyne, 2006). The two
marital quality components predicting best in this
study—the observed affective quality of the couple's
actual interaction (positivity/negativity ratio) and the
reported frequency of their “useful
discussions” about the patient's
illness—illustrate potentially distinct processes
through which marital quality may have its effect.
On the one hand, consistent with other research on marriage and
health, receipt of social emotional support or exposure to
marital conflict could buffer or exacerbate stress via direct
physiological pathways (Gallo, Troxel, Matthews, & Kuller,
2003; Kiecolt-Glaser & Newton,
2001; Ryff & Singer, 2000).
The useful-discussion finding, on the other hand, highlights a
less studied and more instrumental dimension of couple coping
behavior reminiscent of what Lyons, Mickelson, Sullivan, and Coyne
(1998) called “communal
coping” (p. 579; cf. Acitelli & Badr, 2005;
Berg &
Upchurch, 2007; Bodenmann, 2005;
Lewis et al.,
2006). In essence, such a communal or
cooperative problem-solving process involves appraising a
stressor (the patient's heart condition) as
“our” issue rather than
“yours” or “mine” and
taking cooperative “we”-based action to
address it (Lyons et
al., 1998).
Although marital researchers have used both observational and
self-report methods to link communal aspects of specific dyadic
relationships to relationship outcomes (Acitelli & Badr,
2005; Buehlman, Gottman, & Katz,
1992; Mills, Clark, Ford, & Johnson,
2004), recent developments in the arena of
automatic text analysis may hold special promise for
illuminating connections between close relationships and health
(Pennebaker, Mehl,
& Niederhoffer, 2003;
Simmons, Gordon,
& Chambless, 2005;
Slatcher &
Pennebaker, 2006). In particular, use of
first-person plural pronouns (we,
us, our) in the context of
couple communication appears to mark relational commitment,
shared identity, and effective problem solving by relationship
partners (Agnew, Van
Lange, Rusbult, & Langston,
1998; Simmons et al., 2005). In the
present study, we hypothesize that partners' we
talk might also mark an effective communal approach to coping
with a serious health problem such as chronic heart failure,
paying dividends in terms of predicting a favorable course of
the patient's illness.
At an individual level, research using automatic text analysis
software such as Linguistic Inquiry and Word Count (LIWC;
Pennebaker,
Francis, & Booth, 2001) has
shown that easily countable linguistic features of transcribed
narratives can predict (or postdict) such diverse aspects of
adaptation as physical health change (Pennebaker, Mayne, & Francis,
1997), responses to trauma
(Cohn, Mehl,
& Pennebaker, 2004;
Stone &
Pennebaker, 2002), recovery from
anorexia (Lyons, Mehl,
& Pennebaker, 2006), and even
whether poets commit suicide (Stirman & Pennebaker,
2001). It is important to note that a unique
methodological advantage of a text analysis approach to studying
coping and support processes may be that it is less vulnerable
to social desirability bias than traditional interview and
questionnaire approaches are, especially for measuring a highly
evaluative construct such as the quality of social relations
(Pressman &
Cohen, 2007).
In a review of word-use research, Pennebaker et al.
(2003) emphasized the value of studying
particles, that is, filler parts of speech (e.g., prepositions,
articles, and especially pronouns) that linguistically carry
little or no conversational content but on a psychological level
often serve to mark emotional states, social identity, and
cognitive styles. Because pronouns and other particles reflect
linguistic style rather than content, they may be less a product
of conscious word choice than regular verbs or nouns (e.g.,
emotion words) are and thus may reflect more fundamental
psychosocial processes (Pennebaker et al., 2003). Thus, in one
of few applications of text analysis to heart patients,
Scherwitz and colleagues linked
self-involvement, defined by elevated frequency
and density of first-person singular pronoun use, to Type A risk
behavior and clinical outcomes of coronary heart disease
(Scherwitz
& Canick, 1988). Other studies
show shifts from first-person singular to plural pronoun use by
bloggers (Cohn et al.,
2004) and former mayor Rudolph Giuliani
(Pennebaker
& Lay, 2002) coincident with the
communal crisis of September 11, 2001, suggesting that
I talk and we talk may be
subtle markers of individual versus communal self-construal, at
least in the context of talking about an upsetting life event.
Indirect evidence that pronoun use could have adaptive
significance for couples came from Buehlman et al.'s (1992)
observational coding of we-ness versus
separateness during conjoint oral-history interviews.
Judges' ratings of couple we-ness, based largely on partners'
tendency to use we rather than
he, she, or I
when recounting their marital history, correlated with
concurrent positive interaction behavior and predicted both
marital satisfaction and divorce over the next 4 years (cf.
Gottman &
Levenson, 1999). Unfortunately, because
pronoun use was only one component of the we-ness measure, the
Buehlman et al. results do not demonstrate that
we talk alone predicted positive marital
outcomes.
More definitive data on pronouns in marital interaction come
from Simmons et al.'s (2005) study comparing LIWC pronoun counts
with observed and reported marital quality in a sample of 59
couples where one spouse had a diagnosed anxiety disorder.
During face-to-face problem-solving discussions, first-person
plural pronoun use by both partners correlated with
independently coded positive problem-solving behavior during the
same interaction, even after controlling for general behavioral
negativity, which correlated strongly with second-person
you talk. Although we
talk was not related to marital satisfaction, Simmons et al.
(2005) concluded that first-person plural
pronoun use does predict (or at least reflect) better
cooperative problem solving—and, consistent with our
communal coping hypothesis, they suggested that partners who
used such pronouns more often “had a greater sense of
shared responsibility or stake in the problem discussed, which
may have helped them collaborate more effectively” (p.
935).
The present study differs from and extends previous plural
pronoun research in a number of ways. First, our dependent
variables concern the status and course of a serious physical
health problem rather than concurrent marital functioning or
later marital outcomes. Chronic HF, the end stage for many forms
of heart disease, is an increasingly prevalent, costly condition
that makes stringent demands on patients and their families
(MacMahon &
Lip, 2002; Rohrbaugh et al.,
2002). Although treatments have improved
dramatically, HF continues to be associated with shortened life
expectancy (up to 50% mortality 5 years after diagnosis),
frequent hospitalizations, and diminished quality of life
(Chin &
Goldman, 1998; Masoudi et al.,
2004).
Second, rather than studying problem-solving discussions about
relationship conflicts in the laboratory (as Simmons et al.,
2005, did), we examined partners'
first-person pronoun use during an open-ended home interview
about how they cope(d) with the patients' heart failure. Thus,
in line with Buehlman et
al. (1992), the conversations were more
interviewer focused than partner focused and typical of what a
clinician might encounter during an assessment or therapy
session.
Third, we compared nonreactive pronoun markers of communal
coping derived from automatic text analysis with a direct
self-report measure of the same construct. If the text-analysis
approach is indeed more implicit and less vulnerable to
social-desirability reporting bias (Pennebaker et al.,
2003; Pressman & Cohen,
2007), the we talk measures
may have greater prognostic value than self-reported communal
coping or relationship quality for predicting future health
change.
Fourth, in addition to analyzing singular and plural
first-person pronouns in the aggregate, we distinguished their
active and passive forms (I talk vs.
me/my talk,
we talk vs.
us/our
talk). As Pennebaker et al. noted in their
(2003) review, interest in the active–passive
pronoun-use distinction goes back at least to William
James
(1890), although its pragmatics remain
largely uninvestigated. Here, in keeping with the distinction
between active and passive forms of coping with health problems,
we examined possible differences in the adaptive implications of
we talk and
us/our talk (as well as
I talk and
me/my talk) for patients with
HF.
Fifth, we considered the possibility that personal pronoun use
by the patient's spouse, as well as by the patient, makes an
independent contribution to predicting the course of cardiac
illness. Precedence for distinguishing such partner effects and
actor effects with HF patients comes from our studies of the
Michigan sample referenced in the first paragraph, where the
spouse's confidence in the patient's ability to manage the
illness predicted 4-year survival over and above what the
patient's own self-efficacy ratings could predict
(Rohrbaugh et al.,
2004). Would a spouse's communal
orientation to coping with HF likewise have as much or more
adaptive significance than a communal orientation by the
patient? Or does effective communal coping require balanced
we talk by both partners (i.e., is it a
true couple-level phenomenon)? These, too, are questions the
present study attempts to address.
Finally, although the present (Arizona) sample included
relatively few female patients, gender implications of
we talk are difficult to ignore. Apart from
apparent gender differences in overall pronoun use
(Mehl &
Pennebaker, 2003; Newman, Groom, Handelman, &
Pennebaker, 2008) and possibly inherent
gender differences in communal orientation (Taylor, 2006), the
Michigan HF studies found marital functioning more important to
the survival of female patients than male patients
(Coyne et al.,
2001; Rohrbaugh et al., 2006; cf.
Krumholz et al.,
1998). The latter finding is consistent
with a broader literature suggesting that associations between
marital quality and health tend to be stronger for women than
for men (Kiecolt-Glaser
& Newton, 2001;
Saxbe, Repetti,
& Nishina, 2008). If marital
we talk does predict how patients adapt to
heart disease, the results might also suggest possible gender
differences that could involve either the recipient or the
provider of communal coping.
To summarize, we hypothesized that first-person plural pronoun
use (we talk) by one or both partners during an
open-ended conjoint interview would predict the course of the
patient's HF symptoms over a 6-month period and do so better
than (or at least independently from) direct self-reports of
marital quality and communal coping. Additional exploratory
questions, for which specific hypotheses would be premature,
concerned (a) the relative contributions of we
talk by patients and spouses (whether independent actor or
partner effects emerge and whether adaptive we
talk is a couple-level phenomenon), (b) relative contributions
of the active first-person plural pronoun form
(we) compared with passive first-person plural
forms (us, our), and (c) the
possible role of gender in moderating predictive associations
between pronoun use and patient health change.
Method Overview
HF patients and their spouses
participated in a brief, open-ended interview focusing on
how they had coped with the patients' heart conditions. We
then used first-person pronoun patterns (singular vs.
plural, active vs. passive) derived from LIWC analysis of
each partner's interview responses to predict changes in the
patient's symptom severity and general health over the next
6 months. Baseline self-report measures of marital quality,
communal coping, and psychological distress were available
as well, and we examined these as potential covariates of
pronoun use that might explain predictive associations with
the patient's well-being.
Participants
Participants were 60 HF patients (43 men,
17 women) and their opposite-sex spouses recruited primarily
from University of Arizona cardiology clinics. All patients
carried a confirmed HF diagnosis and had a left ventricular
ejection fraction (LVEF), usually documented by
echocardiogram during the previous 6 months, of less than or
equal to 40 (M = 29.1, SD
= 8.7). At the time of the home interview, mean New York
Heart Association (NYHA) functional class was 2.3
(SD = 0.8) on a 1–4 scale,
with 13.3%, 55.0%, 20.0%, and 11.6% of the patients in
Classes I, II, III, and IV, respectively (Domanski, Garg, & Yusuf,
1994). On average, HF had been
diagnosed 4.8 years earlier (SD = 5.1) and
heart problems 11.5 years earlier (SD =
9.8). HF is a complex diagnosis with diverse etiology, and
we do not have systematic information on the variety of
etiological pathways represented in the sample. Available
data do, however, indicate that almost half (42%) of the
patients had experienced myocardial infarction, and
prevalence rates for diabetes and hypertension were 32% and
25%, respectively. Although 50% of the patients had been
hospitalized and 42% had made an emergency room visit in the
previous 6 months, all were outpatients at the time of the
home interview.
Mean ages of patients and spouses were 67
(SD = 11.7) and 65.6
(SD = 10.7) years, respectively, and
couples had been married an average of 34.8 years
(SD = 16.7). The patient sample was
predominantly White (85%), well-educated (40% were college
graduates), and affluent (M zip code income
was at the 65th percentile in the year 2000).
Procedure
During visits to each couple's home,
research assistants interviewed the patient and spouse both
separately and conjointly. Speech samples for LIWC analysis,
transcribed separately for each partner, came from responses
to two open-ended questions asked in conjoint format near
the end of the home visit: (a) “As you think back
on how the two of you have coped with the heart condition,
what do you think you've done best? What are you most proud
of?” and (b) “Looking back on your own
experiences, what suggestions or advice could you offer
other heart patients and their families?”
Interviewers directed these questions to the couple,
encouraged elaboration with reflective prompts, and allowed
time for both partners to answer. Two couples did not
participate in the conjoint coping interview because of
patient fatigue, and the audio recorder malfunctioned for a
third couple, so a total of 57 transcripts (from 41 male-
and 16 female-patient couples) were available for analysis.
The baseline home visit also provided
detailed assessments of the patient's HF symptoms (rated by
both partners) and his or her general health, measured by
the 36-Item Short-Form Health Survey (SF-36;
McHorney, Ware,
& Raczek, 1993). Other
psychosocial variables included the patient's perceived
self-efficacy to manage the illness and parallel
(individual) reports of communal coping, marital quality,
and psychological distress obtained from each partner. Six
months later, in separate telephone interviews with both
partners, we again assessed the patient's HF symptoms and
general health.
Participants provided their informed
consent at the beginning of the home interview. All aspects
of the study were conducted in compliance with procedures
established by the University of Arizona Human Subjects
Committee.
Measures
Pronoun use
Automatic text analyses performed
with the LIWC software (Pennebaker et al.,
2001) produced separate counts of
all pronoun types used by the patient and spouse in each
couple. The LIWC presents variables in a relative
metric, as percentages of a participant's total number
of transcribed words, and a separate pronoun dictionary
permitted further distinctions among active versus
passive personal pronouns. To address the main research
questions, we focused narrowly on first-person pronouns
and based statistical analyses on proportion variables
calculated to represent (a) each partner's use of
first-person plural (we talk) and
singular (I talk) pronouns relative to
all personal (first-, second-, and third-person)
pronouns; (b) each partner's use of first-person
pronouns that were plural rather than singular
(we/I ratio), with total
first-person pronouns as the denominator; and (c) each
partner's use of pronouns that were plural active
(we active) plural passive
(us/our passive), singular active
(I active), and singular passive
(me/my passive), again with total
first-person pronouns as a denominator. The first set of
pronoun variables allowed for examining patient and
spouse I talk and we
talk independently in the same analysis, whereas the
remaining variables (we/I ratio,
we active,
us/our passive,
etc.) captured the relative balance of plural versus
singular first-person pronouns. We also examined total
words and total pronouns as covariates of the proportion
variables, although this had little impact on the
results.
Report measures of communal coping
The individual patient and spouse
interviews each included two questions based on the
Lyons et
al. (1998) communal coping
construct. The patient items were (a) “When
you think about problems related to your heart
condition, to what extent do you view those as ′our
problem' (shared by you and your spouse equally) or
mainly your own problem?” and (b)
“When a problem related to your heart
condition arises, to what extent do you and your partner
work together to solve it?” Both items had a
1–5 response scale, bipolar in the first case
(from 1 = my problem to 5 = our
problem) and unipolar in the second (from 1
= not at all to 5 =
always). Items for the spouse were
directly parallel but referred to “your
partner's heart condition.” Although the two
items correlated only modestly (r = .41
for patients and .26 for spouses), we nonetheless
averaged them to provide a self-report communal
coping score for each partner (patient
M = 4.1, SD = 1.0;
spouse M = 4.6, SD =
0.6).
Patient outcomes
The two dependent variables, each
related to the patient's health and assessed at both
baseline and follow-up, were HF
symptoms and SF-36 health
status. The measure of HF symptoms reflected
patient and spouse ratings of the extent to which the
patient experienced eight specific symptoms in the
previous month. The symptoms were (a) fatigue or lack of
energy for normal activities; (b) difficulty breathing,
especially with exertion; (c) waking up breathless at
night; (d) swelling in ankles and feet; (e) chest pain;
(f) heart flutter (fibrillation); (g) dizziness or
fainting; and (h) nausea, with abdominal swelling or
tenderness. The patient and spouse conjointly rated the
presence of each symptom on a three-level scale
(not at all, some,
a lot) during the baseline
interview, and they did so again separately at
follow-up, with good interrater agreement (intraclass
r = .73). Half of the patients
(n = 30) also completed the Kansas
City Cardiomyopathy Questionnaire (Green, Porter, Bresnahan,
& Spertus, 2000) at
baseline, and the Functional-Status
scale from this validated instrument correlated highly
with our HF symptoms measure (r =
−.79). Internal consistency of the HF symptoms
scale was good at both baseline (α = .79) and
follow-up (α = .84). Mean symptom scores, based
on summing averaged patient and spouse scores, were 15.0
(SD = 3.7) at baseline and 12.3
(SD = 2.8) at follow-up.
To capture the patient's overall
health, we used physical and mental component summary
scores from the widely used SF-36 (McHorney et al.,
1993; Ware, 2000). At
baseline, norm-based scores for physical and mental
health were 43.0 (SD = 19.1) and 56.7
(SD = 19.4), and, at follow-up, the
respective scores were 49.2 (SD = 24.2)
and 67.0 (SD = 21.1). Because scores
for the physical and mental components correlated highly
at baseline (r = .63) and follow-up
(r = .78), we averaged them into a
composite SF-36 health status variable for the main
analyses.
Not surprisingly, HF symptoms and
SF-36 health status also correlated significantly and
substantially with each other at both baseline
(r = −.57,
p < .001) and follow-up
(r = −.66,
p < .001). Test–retest
stability for the two measures was moderately high
(rs = .64 and .71, respectively),
and residual scores reflecting change from baseline to
follow-up correlated as well (r = .53,
p < .01). Less expected were
significant improvements from baseline to follow-up for
both HF symptoms, t(56) =
−8.76, p < .001, and
SF-36 health, t(56) = 4.05,
p < .001.
Marital quality and psychological distress
The patient and spouse also completed
two brief measures of marital quality:
Hendrick's
(1988) seven-item Relationship
Assessment Scale (RAS) and Heavey, Larson, Zumtobel, and
Christensen's (1996) seven-item
Constructive Communication Scale (CCS). Internal
consistency was good for both measures (all αs
> .80). In addition, mean RAS item scores for
patients and spouses were near the upper end of the
1–5 response scale (Ms = 4.4
and 4.4, SDs = 0.6 and 0.7,
respectively), suggesting that couples in this sample
tended to be fairly well satisfied with their
longstanding marriages. Because correlations between RAS
and CCS scores were moderately high for both patients
(r = .61) and spouses
(r = .64), we used the mean
z score for these two variables as an
index of marital quality for each
partner.
The last variable examined as a
possible covariate of first-person pronoun use was
psychological distress,
operationalized via a 25-item version of the Hopkins
Symptom Checklist (HSCL-25) used in previous research
with HF couples (Rohrbaugh et al.,
2002). As in previous studies,
internal consistency was high (α = .93), and a
nontrivial proportion of participants (37% of the
patients and 20% of their spouses) scored in a range
associated with a diagnosis of anxiety or depression
(Hesbacher,
Rickels, Morris, Newman, & Rosenfeld,
1980).
Results Patterns of Pronoun Use
Descriptive statistics for patient and
spouse pronoun variables appear in Table 1. In the top panel, raw
pronoun proportions based on total word counts indicate that
first-person plural pronouns (we,
us, our) occurred with
relatively low frequency, making up less that 2.5% of all
transcribed words from the interview. In fact, 7 patients
and 3 spouses used no plural first-person pronouns at all.
The relative first-person proportion variables in the bottom
part of Table
1 (we talk,
I talk, we/I
ratio, etc.) have higher values, reflecting different
denominators, and were used in the main analyses. Because
the distributions of these variables tended to be negatively
skewed, we applied arcsine transformations to improve
normality and used these transformed values in all analyses.
Means, Standard Deviations, and Ranges of Pronoun Variables for
Patients and Spouses
To examine mean-level differences in
pronoun use and total word use, we performed mixed-model
analyses of variance (ANOVAs) with couple as the unit of
analysis (Maguire,
1999) using the SPSS 16 general
linear model statistical module. The speaker's role (patient
vs. spouse) was a within-couple effect in these models,
whereas patient gender was a between-couple effect. Some of
the models also included pronoun type (e.g.,
I talk vs. we talk,
we active vs. we
passive) as a within-case variable to examine possible main
effects and interactions involving this factor. Although
ANOVAs for total word count and total personal pronouns
revealed no significant main effects or interactions, those
comparing pronoun types found greater use of first-person
pronouns by patients than spouses, F(1, 56)
= 12.2, p < .001, and greater use of
both second-person pronouns, F(1, 56) =
7.36, p = .009, and third-person pronouns,
F(1, 56) = 4.83, p =
.032, by spouses compared with patients.
ANOVAs focusing specifically on
first-person singular and plural pronouns found significant
within-case effects for role and pronoun type(s) as well as
several Role × Type interactions. Thus, including
transformed we talk and I
talk proportions in the same analysis confirmed the higher
prevalence of I talk, F(1,
55) = 166.06, p < .001, and yielded
a significant Role × Type interaction,
F(1, 55) = 20.57, p
< .001. Tests for simple effects related to this
interaction revealed significant differences for all pairs
of means, with patients showing more I talk
than spouses (M = 43.0 vs. 28.8,
p < .001; see Table 1) and
spouses showing more we talk than patients
(M = 12.7 vs. 9.3, p =
.024). A similar ANOVA incorporating the
active–passive dimension (e.g.,
we active,
us/our passive) likewise
indicated a preponderance of active over passive pronouns,
F(1, 55) = 246.28, p
< .001. In addition, a significant Role ×
Type (active–passive) interaction for plural
pronouns reflects differential use of we
active and us/our passive
pronouns by spouses and patients, F(1, 55)
= 13.76, p < .001, with spouses
exceeding patients in we active talk
(M = 25.1 vs. 14.4, p
< .001) but not in
us/our talk
(M = 4.3 vs. 3.2, p =
.167). Strikingly, the patient's gender, either alone or in
interaction with role and/or pronoun type, had no
significant statistical effects in any of these analyses
(ps > .1). In other words,
gender appeared to make little difference in how patients
and spouses used personal pronouns when discussing the
patients' illness.
Further pronoun analyses examined
correlations between the proportion measures and between the
spouses. Not surprisingly, the we talk and
I talk proportions tended to correlate
negatively with each other, although somewhat more so for
patients (r = −.44,
p < .001) than for spouses
(r = −.20, p
> .1). However, there was essentially no association
between patient and spouse pronoun use for
we talk (r =
−.09), I talk (r
= .05), or the we/I ratio
(r = −.05). Thus, if plural
pronouns do mark communal coping, they appear to do so in a
manner that does not represent a reciprocal, couple-level
process.
Other Correlates of We Talk and Patient Health Change
The last set of preliminary analyses
aimed at identifying correlates of the we
talk predictor variables and of change in the two dependent
patient-health variables from baseline to follow-up. Here we
were interested not only in demographic and clinical
characteristics but also in the self-report measures of
communal coping and marital quality that relate most
directly to the study hypotheses. One reason for doing this
was to identify possible control variables (covariates) that
might later explain links between we talk
and health change.
On the one hand, correlational analyses
found essentially no relationships between the
we talk indices and either partner's
gender, age, or education, nor were there any significant
associations between we talk and concurrent
clinical variables such as HF symptoms, SF-36 health status,
NYHA class, LVEF, illness duration, recent hospitalization,
or the patient's psychological distress. On the other hand,
the self-report measure of communal coping did tend to
correlate with the we/I
ratio for patients (r = .32,
p < .05), although not for spouses
(r = .13, ps >
.1), and the we/I ratio
correlated with the marital quality scores of both partners
(rs = .28 and .26, respectively,
ps < .05). It is interesting
that actor–partner regression analyses indicated
also that one partner's plural pronoun use predicted the
other's perception of marital quality over and above any
actor effect (bs for both
we/I ratios = .32,
ps < .05).
To identify possible predictors of
patient health change, we computed partial correlations with
the respective dependent variables, controlling their status
at baseline. Although some clinical variables (e.g., NYHA
class, LVEF, psychological distress, marital quality)
correlated substantially with patient health at baseline,
only two—marital quality and psychological
distress—appeared to predict HF symptoms or SF-36
health status prospectively: Partial rs
were −.48 and −.42 for patient and
spouse marital quality predicting HF symptom change
(ps < .01), .35 and .35 for
HSCL-25 patient and spouse distress predicting HF symptom
change (ps < .05), and .30 and .40
for marital quality predicting SF-36 health change (spouse
p < .05).
Of note, the self-report measure of
communal coping was unrelated to HF symptoms at baseline
(rs = −.15 and −.09
for patients and spouses), and it did not predict change in
HF symptoms during the follow-up period (partial
rs = −.03, −.01).
Communal coping did, however, correlate with reported
marital quality (rs = .59 and .39 for
patients and spouses, ps < .05),
suggesting that, in the self-report domain, these are
related but distinct constructs (cf. Bodenmann,
2005).
Pronoun Predictors of Patient Health Change
To address the main research questions,
we first performed a multiple regression analysis for each
of the two patient outcomes, with patient and spouse
we talk, patient and spouse
I talk, and the patient's gender as
predictors. The regression models also included a baseline
measure of the relevant outcome to capture residualized
change, and predictors were centered to minimize
collinearity of interaction terms (Aiken & West,
1991). Despite our interest in gender as
a putative moderator, no two- or three-way interactions
involving pronoun variables and the patient's biological sex
were statistically significant in these (or any other)
analyses, so we report simplified regression results with
gender excluded. Similarly, because including total word
count and/or personal pronouns as covariates had no
appreciable effect on regression results, we exclude those
variables as well.
Table 2 presents standardized beta weights
from the regression analyses for the two patient health
outcomes. Note here that negative beta weights reflect
positive (healthy) change for HF symptoms (i.e., symptom
reduction), whereas the opposite applies for SF-36 health
status. For both dependent variables, we
talk by the spouse predicted positive changes in the
patient's health independent of what the patient's own
plural pronoun use predicted. In fact, the spouse's
we talk was the only significant
predictor of change in the patient's HF symptoms, although
betas for I talk by both partners were also
in the direction of predicting positive change in the
patient's general health. In neither case, however, was the
patient's we talk associated with his or
her own health change, and partner (spouse) effects on the
patient's well-being were generally more in evidence than
actor effects.
Standardized Betas and Significance Levels for Pronoun Predictors
of Patient Health Change
Follow-up analyses narrowed the focus of
prediction to relative use of plural versus singular
first-person pronouns. Including both partners'
we/I ratio scores appeared to sharpen the
spouse (partner) effect for HF symptoms (b
= −.33, p = .002) but weaken it
for the SF-36 measure of general health (b
= .10, p > .10). Incorporating the
active–passive dimension suggested further that
we active pronouns predicted HF
symptoms (b = −.24,
p = .034), whereas
us/our passive
pronouns did not (b = −.13,
p > .10), although parallel
effects were again not evident for SF-36 health status (both
bs = .02 and .15, respectively,
ps > .10).
Additional regression analyses explored
the relative contributions of patient and spouse
we talk directly, along with the predictive
potential of combining the two partners' scores. For HF
symptoms, a partner discrepancy score created by subtracting
the patient's we/I ratio
score from the spouse's score showed a significant effect on
patient health change (b = −.27,
p = .011), whereas a couple-level
(mean) we/I ratio had a
somewhat weaker effect (b = −.18,
p = .085). As with other analyses based
on the we/I ratio, there
were no associations between these couple-level variables
and changes in the patient's general (SF-36) health. In
summary, we found little evidence that patient and spouse
we talk had interchangeable
consequences for the patient's health.
Pronoun Prediction With Additional Covariates
A final set of regression analyses tested
the possibility that third (control) variables might account
for the associations between we talk and HF
symptom change and compared the prognostic significance of
pronoun and self-report indicators of the communal coping
construct. To pursue this, we focused on the
we/I ratio, the
pronoun variable most predictive of HF symptom change, and
used stepwise regression to examine the effect of adding a
control variable to the actor–partner model. On
the basis of finding relatively few substantial correlates
of we talk and/or symptom change, one might
expect these analyses to have low yield, which, in fact, was
the case. None of the clinical variables we examined as
potential covariates (e.g., NYHA class, LVEF, illness
duration, psychological distress) reduced the statistical
partner effect of spouse
we/I ratio, nor did either
the self-report measure of communal coping, which, as noted
above, had no direct main effect on symptom change, or the
partners' reports of marital quality that, when taken
separately, had predicted the symptom-change criterion.
DiscussionThe results suggest that we talk in couples
coping with HF has prognostic significance for the patient's
health. As hypothesized, the use of first-person plural pronouns
during a conjoint discussion about coping with the patient's
heart condition predicted positive change in HF symptoms over
the next 6 months. Strikingly, however, this result appeared for
we talk by the spouse and not the patient,
creating a statistical partner effect in the absence of a
corresponding actor effect. Exploratory analyses suggested
further that the spouse using the active first-person plural
pronoun (we) contributed more to predicting
symptom change than passive first-person plural forms
(us, our), although this
was not the case for predicting change in the patient's general
health. The patient's (or spouse's) gender, however had no
moderating effects on any results obtained.
More broadly, the we talk findings provide
further evidence that a communal orientation by at least one
partner in a committed relationship can have adaptive
consequences, not only for the dyad as a unit
(Buehlman et al.,
1992; Simmons et al., 2005) but also
for the other partner's health. This highlights an instrumental
dimension of coping with chronic health problems, grounded in
specific dyadic processes (Berg & Upchurch, 2007;
Revenson, Kayser,
& Bodenmann, 2005), that
compliments a much larger body of research on marital conflict
and receipt of social emotional support. From a methodological
perspective, the results also add to growing evidence of
transitive partner effects on individual health
(Ruiz, Matthews,
Scheier, & Shulz, 2006; cf.
Rohrbaugh et al.,
2004), wherein one person's behavior
predicts another person's health outcome over and above what the
same behavior by the actor can predict (Kenny, 1996). The
presence of such statistical partner effects implies
interpersonal influence but usually leaves the mechanism of that
influence unclear.
Perhaps the most important methodological implication of this
preliminary study concerns the potential utility of automatic
text analysis in research on couples and health. Because most
studies in this area rely on self-reports of key constructs such
as marital quality and coping styles, measurement may be
vulnerable to social-desirability reporting bias in ways that
automatic text analysis is not (Pennebaker et al., 2003;
Pressman &
Cohen, 2007). Consistent with this idea,
our results suggest that we talk as an implicit
marker of communal coping had greater prognostic value in
predicting the course of HF symptoms than either direct
self-reports of the same construct (communal coping) or reports
of general marital quality. However, this finding held for only
one partner in the couple (the spouse), and ambiguities remain
about how best to validate plural pronoun use as a marker of the
communal coping construct (e.g., we talk did
correlate significantly with direct reports of communal coping,
but only for patients).
It seems likely that adaptive implications of marital pronoun
use depend on situational factors such as the nature of
predicted outcomes and the interactional contexts from which
speech samples are derived. Extrapolating from previous
research, one would expect we talk by both
partners to predict future marital stability (Buehlman et al.,
1992) or correlate with concurrent adaptive
problem solving (Simmons et al., 2005). But when the
criterion (outcome) variable concerns one person's health
problem and the pronoun speech sample focuses on that, an
asymmetrical pattern of prediction from partner pronoun use may
be less unusual.
Other important boundary conditions for our findings involve
the manner of eliciting partner speech samples. For example, in
contrast to Simmons et
al.'s (2005) procedure, where couples
discussed a disagreement with no interviewer present, the
pronoun samples in our study came from responses to supportive,
open-ended interview questions about how the couple had coped
with the patient's illness. Compared with a conflict discussion,
our interview questions probably pulled for more positive,
collaborative verbalizations—perhaps especially from a
patient's spouse, whose role as a helper may be implicitly
defined by questions about coping with the patient's illness. In
any case, the present results leave open the question of whether
we talk sampled in a different, less
collaborative context would similarly predict patient health
outcomes. Further, it is not known how stable (traitlike)
we talk proportions might be across
different interactional contexts—for example, when
partners talk to versus about each other with an interviewer
absent versus present during a conflictual versus cooperative
task. These are questions for future research.
The results also hint that the relative frequency of
we talk and I talk (the
we/I ratio variable)
predicted change in HF symptoms better than it did change in the
patient's general health, a difference that was less evident
when we analyzed we talk and I
talk separately. It may be, therefore, that we
talk in the context of discussing a specific illness has
particular prognostic significance for coping with that illness.
Consistent with this idea, the patient's response to a single
self-efficacy follow-up question (“How confident are
you that you can do what you need to do to manage your
illness?”) showed essentially the same partner effects
for spouse we-ratio as the HF symptoms measure
did.
Still, the results are ambiguous about the extent to which
we talk marks the kind of communal coping
construct we envisioned. On the basis of the Michigan study
findings, particularly those linking useful discussions to
patient survival (Rohrbaugh et al., 2006), we had
conceptualized communal coping as a key couple-level component
of marital quality, but this does not fit well with either the
asymmetry of patient and spouse prediction results (partner
effects with no actor effects) or the fact that patient and
spouse we talk scores were essentially
uncorrelated. An alternative interpretation is that
we talk in the context studied here says most
about the caretaking posture of an individual
partner—in this case, the patient's spouse. This was
especially evident in the higher rates of I
talk by patients and we talk by spouses, which
fits the idea that interview questions about the patient's
illness served to highlight the spouse's support role.
Also unexpected was the absence of detectable gender
differences: Male- and female-patient couples did not differ in
how often the partners used various types of pronouns, and, more
relevant to clinical concerns, there was no gender moderation of
any predictive association between we talk and
patient health change. On the basis of previous research (e.g.,
Kiecolt-Glaser
& Newton, 2001;
Rohrbaugh et al.,
2006), one would expect a marital
process reflecting relationship quality (if we
talk indeed represents that) to have greater consequences for
the health of women than for the health of men. Unfortunately,
the imbalance of male and female patients in the sample (41 vs.
16) probably limited our ability to detect such
effects—for example, by restricting the heterogeneity
of spousal support for female patients.
This study has several additional limitations, the most
important of which may be the lack of hard outcome measures not
dependent on participants' self-reports. Although the HF
symptoms measure combined the reports of both partners (and
therefore may be on firmer ground than the patient's solo SF-36
score), we have no assurance that reported symptom change over
only 6 months is itself prognostic of long-term survival, nor is
it clear why the patients' symptoms and general health appeared
to improve over that time. One possible explanation is
methodological, in that the first interview was face-to-face and
the second telephonic. A second is that periodic assessment
contacts with project staff were in some way
therapeutic.
Another study limitation is that the sample was small and
probably not representative of the larger population of HF
patients, even those who are married. For example, compared with
the Michigan sample we studied earlier (Rohrbaugh et al.,
2006), the Arizona patients were more
educated and affluent and apparently also more stable medically,
as suggested by the fact that far fewer of them died in the 2
years following the initial assessment. We may therefore have
sampled a restricted range of (high) couple functioning,
although it is unclear if this would bias the results toward or
away from our findings.
A final limitation, inherent in automatic text analysis itself,
is that simple word counts cannot account for semantic
contextual markers related to dimensions such as irony, sarcasm,
and multiple meanings of the same word (Mehl, 2006).
However, this limitation may be offset by the relative
imperviousness of text analysis to potentially biasing effects
of social desirability and shared method variance. It could even
be that the latter feature helped to make possible our detection
of the dominant (spouse we talk) partner
effect, as shared method variance in self-report studies usually
biases results toward actor effects.
The main clinical implication of this study is that looking
beyond the patient can help to predict the likely course of a
heart patient's health. Specifically, it may be valuable for
clinicians to pay close attention to a spouse's use of
first-person plural pronouns when he or she discusses the
patient's heart condition; open-ended interview prompts such as
those used by our research interviewers should be sufficient for
eliciting this. It is unclear if the results generalize to other
health problems, and we do not yet know how we
talk by one or both partners maps onto other coping attitudes or
behaviors. To the extent that we talk does
reflect a communal orientation to coping, interventions that
specifically attempt to promote such a posture—for
example, by attending to reinforcing partners' recollections of
how they have successfully resolved difficulties together in the
past—may have special benefit for couples coping with
chronic illness (Martire & Schulz, 2007;
Revenson et al.,
2005; Shoham, Rohrbaugh, Trost, & Muramoto,
2006).
Footnotes 1 First-person singular pronouns
(I-focus) in the
Simmons et al.
(2005) study were unrelated to partners'
interaction quality but tended to correlate positively with marital
satisfaction. The latter result is inconsistent with findings by
Sillars, Shellen,
McIntosh, and Pomegranate (1997), who
counted pronouns during a nonclinical marital interaction task that
may have pulled less negativity.
2 Although linguists distinguish these as subjective
and objective pronoun types, we believe
active and passive better
capture their difference at a psychological level.
3 Conceptually, this finding approximates what Kenny (1996) and
others called a statistical partner effect,
distinguished here from the complementary actor
effect represented by the patient's score predicting his or
her own health outcome independent of the spouse's score.
4 This single-item self-efficacy measure was less adequate
psychometrically than the two main dependent variables, and we
excluded it from the main analyses to avoid inflating Type I
error.
5 In
addition to the baseline and follow-up interviews, research staff
had regular phone contact with each patient and spouse for a period
of 2 weeks, beginning about 2 months after the initial assessment,
to collect daily-diary data.
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Submitted: October 22, 2007 Revised: April 7, 2008 Accepted: June 23, 2008
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Source: Journal of Consulting and Clinical Psychology. Vol. 76. (5), Oct, 2008 pp. 781-789)
Accession Number: 2008-13625-007
Digital Object Identifier: 10.1037/a0013238
Record: 43- Title:
- Psychological processes and repeat suicidal behavior: A four-year prospective study.

- Authors:
- O'Connor, Rory C.. Suicidal Behavior Research Laboratory, Institute of Health and Wellbeing, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow, United Kingdom, rory.oconnor@glasgow.ac.uk
Smyth, Roger. Department of Psychological Medicine, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom
Ferguson, Eamonn. School of Psychology, University of Nottingham, Nottingham, United Kingdom
Ryan, Caoimhe. School of Psychology, University of St Andrews, St Andrews, United Kingdom
Williams, J. Mark G.. Department of Psychiatry, University of Oxford, Oxford, United Kingdom - Address:
- O'Connor, Rory C., Suicidal Behavior Research Laboratory, Institute of Health and Wellbeing, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow, United Kingdom, G12 0XH, rory.oconnor@glasgow.ac.uk
- Source:
- Journal of Consulting and Clinical Psychology, Vol 81(6), Dec, 2013. pp. 1137-1143.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 7
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- cognition, defeat, entrapment, longitudinal, suicidal, risk, depression, hopelessness, suicidal ideation
- Abstract:
- Objective: Although suicidal behavior is a major public health concern, understanding of individually sensitive suicide risk mechanisms is limited. In this study, the authors investigated, for the first time, the utility of defeat and entrapment in predicting repeat suicidal behavior in a sample of suicide attempters. Method: Seventy patients hospitalized after a suicide attempt completed a range of clinical and psychological measures (depression, hopelessness, suicidal ideation, defeat, and entrapment) while in hospital. Four years later, a nationally linked database was used to determine who had been hospitalized again after a suicide attempt. Results: Over 4 years, 24.6% of linked participants were readmitted to hospital after a suicidal attempt. In univariate logistic regression analyses, defeat and entrapment as well as depression, hopelessness, past suicide attempts, and suicidal ideation all predicted suicidal behavior over this interval. However, in the multivariate analysis, entrapment and past frequency of suicide attempts were the only significant predictors of suicidal behavior. Conclusions: This longitudinal study supports the utility of a new theoretical model in the prediction of suicidal behavior. Individually sensitive suicide risk processes like entrapment could usefully be targeted in treatment interventions to reduce the risk of repeat suicidal behavior in those who have been previously hospitalized after a suicide attempt. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Cognition; *Risk Factors; *Suicide; Hopelessness; Major Depression; Suicidal Ideation
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Aged; Culture; Depressive Disorder; Female; Follow-Up Studies; Hope; Humans; Internal-External Control; Longitudinal Studies; Male; Middle Aged; Models, Psychological; Motivation; Patient Readmission; Prospective Studies; Recurrence; Risk Factors; Scotland; Suicidal Ideation; Suicide, Attempted; Young Adult
- PsycINFO Classification:
- Behavior Disorders & Antisocial Behavior (3230)
- Population:
- Human
Male
Female - Location:
- Scotland
- Age Group:
- Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs)
Aged (65 yrs & older) - Tests & Measures:
- Defeat Scale
Entrapment Scale
Readmission to Hospital with a Suicide Attempt Measure
Beck Hopelessness Scale
Hospital Anxiety and Depression Scale DOI: 10.1037/t03589-000
Suicide Probability Scale DOI: 10.1037/t01198-000 - Grant Sponsorship:
- Sponsor: Chief Scientist Office, Scottish Government
Grant Number: CZH/4/449
Recipients: No recipient indicated
Sponsor: Wellcome Trust
Grant Number: GRO67797
Recipients: Williams, J. Mark G. - Methodology:
- Empirical Study; Longitudinal Study; Prospective Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Jul 15, 2013; Accepted: May 17, 2013; Revised: Apr 30, 2013; First Submitted: Nov 28, 2012
- Release Date:
- 20130715
- Correction Date:
- 20170220
- Copyright:
- American Psychological Association. 2013
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0033751
- PMID:
- 23855989
- Accession Number:
- 2013-25313-001
- Number of Citations in Source:
- 48
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-25313-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-25313-001&site=ehost-live">Psychological processes and repeat suicidal behavior: A four-year prospective study.</A>
- Database:
- PsycINFO
Psychological Processes and Repeat Suicidal Behavior: A Four-Year Prospective Study / BRIEF REPORT
By: Rory C. O’Connor
Suicidal Behavior Research Laboratory, Institute of Health and Wellbeing, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow, United Kingdom;
Roger Smyth
Department of Psychological Medicine, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom
Eamonn Ferguson
School of Psychology, University of Nottingham, Nottingham, United Kingdom
Caoimhe Ryan
School of Psychology, University of St Andrews, St Andrews, United Kingdom
J. Mark G. Williams
Department of Psychiatry, University of Oxford, Oxford, United Kingdom
Acknowledgement: This research was supported by funding from Chief Scientist Office, Scottish Government (CZH/4/449). J. Mark G. Williams is supported by Grant GRO67797 from the Wellcome Trust. We thank Andrew Duffy of NHS National Services Scotland for conducting the data extraction for the linkage component of the study.
Suicide and self-injurious behavior represent global public health concerns. Previous suicidal behavior is one of the most robust predictors of future suicide and, consequently, it is often the focus of research efforts to better understand the etiology of suicide (Suominen et al., 2004). Although it is generally accepted that distal suicide risk mechanisms may arise from a complex interaction of genetic and environmental factors (Mann, Waternaux, Haas, & Malone, 1999), there is increased recognition that researchers need to move beyond the classic psychiatric diagnostic categories if they are to further understand the etiology of suicide, because the diagnostic categories are not sufficiently sensitive to differentiate the vast majority of people with mental health disorders who do not take their own lives from those who do (Bostwick & Pankratz, 2000; van Heeringen, 2001).
More basic science research into the identification of individually sensitive suicide risk mechanisms (Baumeister, 1990; Joiner, 2005; Nock & Banaji, 2007; Nock et al., 2010; O’Connor, Fraser, Whyte, MacHale, & Masterton, 2008, 2009; Rudd, Joiner, & Rajad, 1996; Van Orden, Witte, Gordon, Bender, & Joiner, 2008; Van Orden et al., 2010; Williams, Barnhofer, Crane, & Beck, 2005; Williams, Van Der Does, Barnhofer, Crane, & Segal, 2008) is vital to inform the development of evidence-informed treatment interventions in this area. One attempt to take account of this literature in a comprehensive way, to specify in detail the development of suicide risk, has been a three-phase psychological model of suicidal behavior, the integrated motivational–volitional model (IMV; O’Connor, 2011).
This model of suicidal behavior (O’Connor, 2011) draws from Williams (1997) and Baumeister (1990) and assumes that both environmentally and biologically mediated risk variables shift individuals through a final common pathway involving a high sensitivity to cues in the environment signaling defeat and a sense of entrapment. It is unique in that it conceptualizes suicide attempts as health behaviors (Ajzen, 1991) with motivational (i.e., factors associated with the development of suicidal thoughts) and volitional (i.e., factors that govern whether suicidal thoughts will be acted on) determinants. It also endeavors to incorporate the key constructs from existing predominant models of suicidal behavior into a process model to inform the development of psychological interventions that reduce the risk of suicide. In the present context, drawing from social rank theory (e.g., Price, Sloman, Gardner, Gilbert, & Rhode, 1994), defeat is characterized by a failed struggle, when an individual has been defeated by a triggering event or circumstances. Entrapment results when one’s attempt to escape from high stress or defeating circumstances (which can be internal or external) is blocked (arrested flight; Gilbert & Allan, 1998; O’Connor, 2003; Pollock & Williams, 2001; Williams, 1997).
Although defeat and entrapment are not new constructs in the psychopathology literature (Baumeister, 1990; Gilbert & Allan, 1998), the findings from a number of independent research groups suggest that they have special relevance in the etiology of suicide (O’Connor, 2011; Rasmussen et al., 2010; Taylor, Gooding, Wood, & Tarrier, 2011; Taylor, Gooding, Wood, Johnson, & Tarrier, 2011; Williams, 1997). In particular, we posit that it is this motivation to escape from the defeating circumstances that drives the search for solutions to end the unbearable psychological pain (Shneidman, 1996) that often characterizes the suicidal mind. Accordingly, as entrapment increases and no solutions are found, the likelihood of suicide being considered an escape strategy increases (O’Connor, 2011; Taylor, Gooding, Wood, & Tarrier, 2011).
Present StudyIn this study, therefore, we aimed to conduct a robust test of the central tenet of the model. Specifically, we aimed to investigate whether, as posited in the IMV model, defeat and entrapment would predict suicide attempts prospectively and that entrapment would be the strongest predictor of repeat suicidal behavior. We have focused on those who have attempted suicide previously because they comprise a high-risk group for suicide. Indeed, a history of repeat suicide attempts is one of the strongest predictors of whether someone dies by suicide (Hawton & van Heeringen, 2009; Owens, Horrocks, & House, 2002). Specifically, we hypothesized that defeat and entrapment would be significant univariate predictors of future suicide attempts (Hypothesis 1). Crucially, though, we also hypothesized (Hypothesis 2) that entrapment would add incrementally to the prediction of suicide attempts, beyond the explanations offered by established predictors of suicidal behavior (e.g., depression, suicide ideation, hopelessness, past suicide attempts).
Method Participants and Procedure
Seventy patients who were seen by the liaison psychiatry service the morning after presenting at a Scottish hospital following a suicide attempt were recruited to the study. The sample was drawn from a larger sample of 136 intentional self-harm patients who were admitted to the hospital. Eighteen participants were excluded because they had been discharged or transferred to another hospital before they could be invited to participate, six were unfit for interview, 33 reported no suicidal intent, and nine declined to participate. The vast majority of patients presented after an overdose (93%; International Classification of Diseases [ICD] Codes ×60–X69), with episodes of self-cutting (n = 3; ICD Codes ×78) and mixed presentations of self-cutting and overdose (n = 4; ICD Codes ×60–X69, ×78) accounting for the remainder of cases. There were 41 females and 29 males with an overall mean age of 35.6 years (SD = 13.24, range: 16–69 years). The men (M = 33.66 years, SD = 11.34) and women (M = 37.07 years, SD = 14.40) did not differ significantly in age, t(68) = 1.07, ns. We did not record ethnicity; however, the overwhelming majority of participants were White.
Baseline data were collected in hospital, usually within 24 hr of admission. The Information Services Division of the National Health Service Scotland maintains a national database of hospital records and mortality data. This nationally linked database is a powerful resource as it allowed us to determine whether a patient was readmitted to hospital in Scotland with intentional self-harm at any time since their index episode. We asked the Information Services Division to extract hospital admissions for intentional self-harm (ICD Codes ×60–X84) in the period between the index suicide attempt and 48 months later for each patient. For this data set, the Information Services Division successfully linked 87% of the sample (61/70). We also reviewed the electronic medical records of those patients who were hospitalized again after intentional self-harm during the follow-up period to determine whether the repeat self-harm episode was a suicide attempt.
Baseline Measures
Depression
The seven-item depression scale from the Hospital Anxiety and Depression Scale (HADS; Zigmond & Snaith, 1983) was used to assess depression. The HADS is a well-established, widely used, reliable, and valid measure of affect (Bjelland, Dahl, Haug, & Neckelmann, 2002; Mykletun, Stordal, & Dahl, 2001) that assesses depression (and anxiety) in psychiatric as well as primary care and general populations. Cronbach’s alpha for the present sample was .80.
Suicidal ideation
Suicidal ideation was assessed using the Suicidal Ideation subscale of the Suicide Probability Scale (SPS; Cull & Gill, 1988). The scale is reliable and valid (Cull & Gill, 1988). The SPS measures an individual’s self-reported attitudes that are related to suicide risk, and the scale has been shown to predict suicide attempts prospectively (Larzelere, Smith, Batenhorst, & Kelly, 1996). Internal consistency for the present study was very good (Cronbach’s α = .86).
Hopelessness
Hopelessness was measured using the 20-item Beck Hopelessness Scale (BHS; Beck, Weissman, Lester, & Trexler, 1974). This reliable and valid measure has been shown to predict eventual suicide (Beck, Steer, Kovacs, & Garrison, 1985; Beck et al., 1974). In the present study, internal consistency was very good (Kuder–Richardson formula 20 = .92).
Defeat
Feelings of defeat were assessed via the Defeat Scale (Gilbert & Allan, 1998). This is a 16-item self-report measure of perceived failed struggle and loss of rank (e.g., “I feel defeated by life”). The Defeat Scale has good psychometric properties (Gilbert & Allan, 1998; Gilbert, Allan, Brough, Melley, & Miles, 2002). It has good test–retest reliability and has been shown to predict suicidality over 12 months independent of baseline levels of depression (Taylor, Gooding, Wood, Johnson, & Tarrier, 2011). Cronbach’s alpha for the present sample was very good (α = .93).
Entrapment
Entrapment represents the sense of being unable to escape feelings of defeat and rejection and is measured by the Entrapment Scale (Gilbert & Allan, 1998). This 16-item self-report measure taps internal entrapment (perceptions of entrapment by one’s own thoughts and feelings) and external entrapment (perceptions of entrapment by external situations). The Entrapment Scale has good psychometric properties (Gilbert & Allan, 1998; Gilbert et al., 2002). It has good test–retest reliability (Taylor, Gooding, Wood, Johnson, & Tarrier, 2011), and it has been shown to distinguish between clinical patients with and without suicide attempt histories (Rasmussen et al., 2010). Cronbach’s alpha for the present study was .91.
Outcome Measure
Readmission to hospital with a suicide attempt
An episode of self-harm was recorded if a patient was admitted to any hospital in Scotland with self-harm in the 48 months after their index episode (ICD Codes ×60–X84 (intentional self-harm). When a patient was readmitted to a hospital with self-harm during the study period, we reviewed their medical records to ascertain whether this episode was a suicide attempt. On admission to the ward, members of the psychiatric team routinely assess suicidal intent. Two trained coders independently rated the medical records and agreed on all 15 positive cases. Coders of repeat suicidal behavior were unaware of all of the baseline measures.
Statistical analyses
We conducted a series of univariate logistic regression analyses for each predictor of a future suicide attempt. Although we are interested specifically in the entrapment and defeat logistic regression analyses, we present the findings for other established predictors of suicidal behavior (i.e., depression, hopelessness, suicide ideation, past suicide attempts). To test the second hypothesis, we conducted a hierarchical multivariate logistic regression including all significant univariate predictors. All analyses were conducted in SPSS 20 and Stata 11.
Results Linked Sample
There were 35 women and 26 men with an overall mean age of 35.6 years (SD = 13.16, range: 16–69 years) in the linked sample. At baseline, 41.4% of these participants (n = 29) reported no previous suicide attempts, 25.7% of participants (n = 18) reported one previous attempt, 10.0% (n = 7) reported two previous attempts, and 22.9% (n = 16) reported three or more previous episodes.
Repeat Suicide Attempt During Follow-Up
Between Time 1 and Time 2 (48 months after the index episode), 32.8% (n = 20) of the linked participants were readmitted to hospital, presenting with intentional self-harm. One participant died by suicide in this time. Of the 20 participants who self-harmed between Time 1 and Time 2, 75% (n = 15) presented with a suicide attempt at follow-up. There was insufficient information to determine suicidal intent for three of the patients and 10% (n = 2) did not report suicide intent at follow-up admissions. Consequently, in the subsequent analyses, these five participants were coded as having made no suicide attempt between baseline and follow-up. In short, 15 participants engaged in a repeat suicide attempt between Time 1 and Time 2. As anticipated, all continuous study measures were intercorrelated (see Table 1).
Correlations, Means, and Standard Deviations for All of the Study Variables for All Participants
Individual and Multivariate Predictors of Suicide Attempts Between Time 1 and Time 2
None of the demographic variables emerged as significant univariate predictors of future suicidal behavior (see Table 2). However, all of the other variables (i.e., defeat and entrapment as well as frequency of previous suicide attempts, suicidal ideation, depression, and hopelessness) individually predicted suicidal behavior between Time 1 and Time 2 (see Table 2).
Univariate Logistic Regression Analyses Investigating Associations Between Baseline Predictors and Hospital-Treated Suicide Attempts or Suicide Between Time 1 (T1) and Time 2 (T2)
To test the prediction that entrapment adds incrementally over depression, we specified suicide ideation, suicide attempt history, and hopelessness in a hierarchical logistic regression, with entrapment and defeat entered at Step 2. Given that there are significant correlations between the predictors raising concerns about multicollinearity, a series of multicollinearity diagnostics were conducted. First, an examination of the correlations derived from the fitted model variance–covariance matrix shows none were greater than .5 (regardless of sign, the mean correlation was .15, the median was .14, with a range of .00 to .33), indicating no collinearity problems. Next, variance inflation factors (VIFs) were examined. These indicate the extent to which the standard errors are inflated because of collinearity. Various rules of thumb for VIFs exist, with some suggesting that VIFs greater than 10 (Hair, Anderson, Tatham, & Black, 1995) and others suggesting VIFs greater than 4 (Menard, 1995) indicate multicollinearity problems. For these analyses, VIFs ranged from 1.16 to 2.93 (M = 2.26) with the square roots of the VIF all less than 2 (M = 1.48, range: 1.08–1.73), indicating that, on average, the standard errors are only inflated 1.48 times because of multicollinearity (Stewart, 1987). Finally, if multicollinearity is a major problem, then odds ratios will be extremely large. This was not the case in these analyses. Therefore, on the basis of the above analyses, there are no problems with multicollinearity.
The results of the hierarchical logistic regression are reported in Table 3 and show that entrapment adds incremental predictive validity over depression, hopelessness, suicide ideation, and the frequency of previous suicide attempts. In the final model, both entrapment and the frequency of previous suicide attempts predict the occurrence of a future suicide attempt. To aid interpretation, Table 3 also reports standardized coefficients for logistic models (see King, 2007; Long, 1997; Long & Freese, 2006; Menard, 2011). The standardized coefficients allow us to examine the relative magnitude of the effects. Given the variety of potential standardized coefficients for logistic regression (King, 2007; Menard, 2011), Winship and Mare (1984) recommended using fully standardized coefficients, and we report fully standardized coefficients as defined by Long and Freese (2006). For the two significant effects, these show that a one standard deviation increase in entrapment results in just over a half a standard deviation increase (.59) in log odds of attempting suicide and a one standard deviation increase in the number of previous attempts results in an increase of one fifth (.20) in log odds of attempting suicide. We also examined if the effect of entrapment in the final model was significantly different from the number of previous attempts. The results showed that although it was stronger, this effect only approached significance, χ2(1), p = .09. However, given the nonlinearity of the logistic model, another way to assess the importance of a predictor is in terms of the discrete change in the predicted probabilities (Long & Freese, 2006). If the predicted probabilities show a large change across the predictor, then it is likely to be an important predictor. For the predictor variables in this model, the largest change was for entrapment, with a predicted probability change of .63 from the minimum value to the maximum of the scale. The next largest was for the number of suicide attempts (.14); the rest ranged from −.02 to .07. This shows entrapment as an important predictor. Examining the effect for entrapment in terms of standard deviation changes from the mean (holding all other variables at their mean) revealed that a single standard deviation increase in entrapment results in a .08 increased probability of attempting suicide.
Hierarchical Multivariate Logistic Regression Analyses Investigating Associations Between Predictors and Hospital-Treated Suicide Attempts or Suicide Between Time 1 and Time 2
DiscussionThis was the first study to investigate the predictive utility of defeat and entrapment among suicide attempters. The findings clearly showed that both defeat and entrapment were significant univariate predictors of suicidal behavior 4 years after an index suicide attempt, alongside depression, hopelessness, suicidal ideation, and previous suicide attempts. It is important to note, though, that consistent with the IMV model (O’Connor, 2011), entrapment was a unique predictor of suicidal behavior when considered together with the other univariate predictors. As frequency of past suicide attempts was the only other significant predictor in the multivariate analysis, entrapment was the only potentially modifiable risk factor for repeat suicidal behavior in this study. The predictive utility of entrapment is consistent with a central tenet of the IMV model of suicidal behavior (O’Connor, 2011), which states that entrapment is a unique predictor of suicidal behavior. According to Gilbert and Allan (1998), it is the thwarted motivation to escape that distinguishes entrapment from hopelessness. Indeed, we posit that as entrapment beliefs become stronger, the motivation to escape increases, and if no solution to the state of entrapment is found, beliefs about suicide become more likely, with suicide being viewed as the only solution to escape the painful feelings of entrapment.
Clinically, these data suggest that it may be useful to incorporate entrapment, together with established predictors, into the psychosocial risk assessment of repeat suicide attempts in patients who have previously been hospitalized after a suicide attempt. Our findings highlight that the former, in particular, may play a unique role within the suicidal process. It may represent part of the final common pathway to suicide. However, little is known about the development of entrapment. Future research, therefore, is required to specify the factors that lead to entrapment as well as the mechanisms accounting for the strong relationship between entrapment and suicidal behavior. Theoretically, the present findings also suggest that the IMV model is a useful new framework that warrants further empirical and clinical investigation. Although there has been a recent suggestion that defeat and entrapment are not distinct constructs (Taylor, Wood, Gooding, Johnson, & Tarrier, 2009), this study reinforces the utility of operationalizing the constructs separately.
Although these findings are promising and the sample size was adequate, the results do require replication and extension. It is also worth noting that this study was set up to investigate the repetition of medically serious suicide attempts: It will have missed low lethality attempts that did not require hospitalization. It also did not record suicide attempts that may have been captured at outpatient clinics, primary care settings, or other nonclinical settings. Researchers conducting future studies should also investigate whether the findings are generalizable to people with baseline attempts that did not result in hospitalization severe enough to result in initial hospitalization. Also, given that the majority of the sample had attempted suicide at least once prior to entry into the study, it would be useful to determine the predictive validity of entrapment in a homogeneous sample of first-time suicide attempters. As entrapment may underpin different types of self-injurious behavior (Nock, 2010; Williams, 1997), future research ought to investigate whether it differentially predicts suicidal versus nonsuicidal self-injury. Finally, large-scale studies are required to determine whether entrapment on its own is predictive of suicide beyond established risk factors.
ConclusionsThis study extends the understanding of individually sensitive mechanisms of suicide risk. The IMV model of suicidal behavior may provide a useful theoretical framework on which clinical formulations and treatment interventions could be based. Entrapment in particular should be included in clinical assessment and considered for inclusion in treatment trials as an index of clinical change. It should also be thought of as potentially part of the final common pathway to serious suicidal behavior.
Footnotes 1 The IMV model is similar to Joiner’s interpersonal–psychological theory (Joiner, 2005; Van Orden et al., 2010), in that both models endeavor to discriminate between those who think about suicide (but do not act on these thoughts, i.e., ideators) and those who act on their thoughts (i.e., suicide attempters). Both models also aim to provide a detailed map of the pathway to suicidal ideation and suicidal behavior, with belongingness and burdensomeness being highlighted in the interpersonal–psychological theory versus defeat and entrapment in the IMV model, in the final common pathway to suicide risk.
2 Intentional self-harm is the terminology used in the ICD and refers to acts of suicidal and nonsuicidal self-harm.
3 We also used receiver operating characteristic curve analysis to identify a cutoff score for each predictor that maximized that predictor’s sensitivity and specificity with respect to predicting a future suicide attempt. The cutoff scores and areas under the curve (AUC) for each predictor were, (a) for entrapment, 51+, AUC = .83; (b) for defeat, 52+, AUC = .83; (c) for hopelessness, 17+, AUC = .82; (d) for depression, 15+, AUC = .01; (e) for ideation, 20+, AUC = .69; and (f) for frequency of previous attempts, 2+, AUC = .79. The hierarchical logistic regression reported in Table 3 was repeated using the predictor score’s case and noncase at these cutoffs. Entrapment and defeat added significantly over the other four variables (Step χ2 = 8.3, p = .016; Model χ2 = 36.2, p < .001). In the final model, both entrapment (B = 2.3, βstdxy = .33, p = .035) and the frequency of previous suicide attempts (B = 2.3, βstdxy = .32, p = .031) were the only significant predictors. Thus, the results were identical to those in Table 3. There were no multicollinearity problems, with a mean VIF of 1.79 (range: 1.56–2.16), and no correlations derived from the fitted model variance–covariance matrix were greater than .5. Although these results replicate the main findings on the basis of continuous scores using binary cutoff scores, we have to caution strongly that these cutoff scores should not, at present, be used for clinical diagnostic purposes, because of the small sample size and number of repeat suicide attempts. These analyses were conducted to show the potential clinical applications of including entrapment as a key predictor of repeat suicide attempts; however, much more work is needed to show that these scales are indeed taxonic and that the cutoffs vary meaningfully with external criteria (see Ferguson, 2009; Ferguson et al., 2009).
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Submitted: November 28, 2012 Revised: April 30, 2013 Accepted: May 17, 2013
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Source: Journal of Consulting and Clinical Psychology. Vol. 81. (6), Dec, 2013 pp. 1137-1143)
Accession Number: 2013-25313-001
Digital Object Identifier: 10.1037/a0033751
Record: 44- Title:
- RCT of web-based personalized normative feedback for college drinking prevention: Are typical student norms good enough?
- Authors:
- LaBrie, Joseph W.. Department of Psychology, Loyola Marymount University, Los Angeles, CA, US, jlabrie@lmu.edu
Lewis, Melissa A.. Department of Psychiatry and Behavioral Sciences, University of Washington, St Louis, MO, US
Atkins, David C.. Department of Psychiatry and Behavioral Sciences, University of Washington, St Louis, MO, US
Neighbors, Clayton. Department of Psychology, University of Houston, Houston, TX, US
Zheng, Cheng. Department of Psychiatry and Behavioral Sciences, University of Washington, St Louis, MO, US
Kenney, Shannon R.. Department of Psychology, Loyola Marymount University, Los Angeles, CA, US
Napper, Lucy E.. Department of Psychology, Loyola Marymount University, Los Angeles, CA, US
Walter, Theresa. Department of Psychiatry and Behavioral Sciences, University of Washington, St Louis, MO, US
Kilmer, Jason R.. Department of Psychiatry and Behavioral Sciences, University of Washington, St Louis, MO, US
Hummer, Justin F.. Department of Psychology, University of Washington, St Louis, MO, US
Grossbard, Joel. Department of Psychiatry and Behavioral Sciences, University of Washington, St Louis, MO, US
Ghaidarov, Tehniat M.. Department of Psychology, Loyola Marymount University, Los Angeles, CA, US
Desai, Sruti. Department of Psychiatry and Behavioral Sciences, University of Washington, St Louis, MO, US
Lee, Christine M.. Department of Psychiatry and Behavioral Sciences, University of Washington, St Louis, MO, US
Larimer, Mary E.. Department of Psychiatry and Behavioral Sciences, University of Washington, St Louis, MO, US - Address:
- LaBrie, Joseph W., Department of Psychology, Loyola Marymount University, 1 LMU Drive, Suite 4700, Los Angeles, CA, US, 90045, jlabrie@lmu.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 81(6), Dec, 2013. pp. 1074-1086.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 13
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- alcohol, college students, personalized normative feedback, social norms, web-based feedback, risky alcohol consumption
- Abstract:
- Objectives: Personalized normative feedback (PNF) interventions are generally effective at correcting normative misperceptions and reducing risky alcohol consumption among college students. However, research has yet to establish what level of reference group specificity is most efficacious in delivering PNF. This study compared the efficacy of a web-based PNF intervention using 8 increasingly specific reference groups against a Web-BASICS intervention and a repeated-assessment control in reducing risky drinking and associated consequences. Method: Participants were 1,663 heavy-drinking Caucasian and Asian undergraduates at 2 universities. The referent for web-based PNF was either the typical same-campus student or a same-campus student at 1 (either gender, race, or Greek affiliation), or a combination of 2 (e.g., gender and race), or all 3 levels of specificity (i.e., gender, race, and Greek affiliation). Hypotheses were tested using quasi-Poisson generalized linear models fit by generalized estimating equations. Results: The PNF intervention participants showed modest reductions in all 4 outcomes (average total drinks, peak drinking, drinking days, and drinking consequences) compared with control participants. No significant differences in drinking outcomes were found between the PNF group as a whole and the Web-BASICS group. Among the 8 PNF conditions, participants receiving typical student PNF demonstrated greater reductions in all 4 outcomes compared with those receiving PNF for more specific reference groups. Perceived drinking norms and discrepancies between individual behavior and actual norms mediated the efficacy of the intervention. Conclusions: Findings suggest a web-based PNF intervention using the typical student referent offers a parsimonious approach to reducing problematic alcohol use outcomes among college students. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Alcohol Drinking Patterns; *Feedback; *Intervention; *Online Therapy; *Social Norms; College Students; Risk Taking
- Medical Subject Headings (MeSH):
- Adolescent; Alcohol Drinking; Feedback, Psychological; Female; Humans; Internet; Linear Models; Male; Social Norms; Social Perception; Students; Young Adult
- PsycINFO Classification:
- Health & Mental Health Treatment & Prevention (3300)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Daily Drinking Questionnaire
Quantity/Frequency Index
Drinking Norms Rating Form DOI: 10.1037/t03956-000
Rutgers Alcohol Problem Index DOI: 10.1037/t00517-000 - Grant Sponsorship:
- Sponsor: National Institute on Alcohol Abuse and Alcoholism
Grant Number: R01AA012547-06A2
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Aug 12, 2013; Accepted: Jul 8, 2013; Revised: Jun 18, 2013; First Submitted: Aug 3, 2012
- Release Date:
- 20130812
- Correction Date:
- 20131202
- Copyright:
- American Psychological Association. 2013
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0034087
- PMID:
- 23937346
- Accession Number:
- 2013-28918-001
- Number of Citations in Source:
- 62
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-28918-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-28918-001&site=ehost-live">RCT of web-based personalized normative feedback for college drinking prevention: Are typical student norms good enough?</A>
- Database:
- PsycINFO
RCT of Web-Based Personalized Normative Feedback for College Drinking Prevention: Are Typical Student Norms Good Enough?
By: Joseph W. LaBrie
Department of Psychology, Loyola Marymount University;
Melissa A. Lewis
Department of Psychiatry and Behavioral Sciences, University of Washington
David C. Atkins
Department of Psychiatry and Behavioral Sciences, University of Washington
Clayton Neighbors
Department of Psychology, University of Houston
Cheng Zheng
Department of Psychiatry and Behavioral Sciences, University of Washington
Shannon R. Kenney
Department of Psychology, Loyola Marymount University
Lucy E. Napper
Department of Psychology, Loyola Marymount University
Theresa Walter
Department of Psychiatry and Behavioral Sciences, University of Washington
Jason R. Kilmer
Department of Psychiatry and Behavioral Sciences, University of Washington
Justin F. Hummer
Department of Psychology, Loyola Marymount University
Joel Grossbard
Department of Psychiatry and Behavioral Sciences, University of Washington
Tehniat M. Ghaidarov
Department of Psychology, Loyola Marymount University
Sruti Desai
Department of Psychiatry and Behavioral Sciences, University of Washington
Christine M. Lee
Department of Psychiatry and Behavioral Sciences, University of Washington
Mary E. Larimer
Department of Psychiatry and Behavioral Sciences, University of Washington
Acknowledgement: Data collection and manuscript preparation were supported by National Institute on Alcohol Abuse and Alcoholism Grant R01AA012547-06A2.
Heavy drinking among college students is associated with a range of serious primary and secondary consequences (e.g., academic and psychological impairment, risky sexual behavior and victimization, car accidents, and violence; Hingson, Zha, & Weitzman, 2009; Wechsler & Nelson, 2008). A considerable body of research confirms that behavioral decisions, such as the decision to drink heavily, are influenced by normative perceptions of significant referents’ behaviors and beliefs (Berkowitz, 2004; Borsari & Carey, 2003). For example, perceptions of peers’ drinking (descriptive norms) and attitudes toward drinking (injunctive norms) have been identified as among the strongest predictors of personal drinking behavior among college students (Neighbors, Lee, Lewis, Fossos, & Larimer, 2007; Perkins, 2002). Students commonly and consistently overestimate the amount of alcohol peers consume (Borsari & Carey, 2003; Lewis & Neighbors, 2004), with approximately seven in 10 students overestimating the amount of alcohol consumed by typical students at their college (Perkins, Haines, & Rice, 2005).
Interventions to correct normative misperceptions can reduce drinking and negative consequences among college students (e.g., Bewick et al., 2010; LaBrie, Hummer, Neighbors, & Pedersen, 2008; Neighbors, Larimer, & Lewis, 2004; Walters, 2000). Personalized normative feedback (PNF) interventions, which attempt to correct normative misperceptions by presenting students with individually delivered feedback comparing their personal drinking behavior, perceptions of peers’ drinking behavior (perceived descriptive norms), and peers’ actual drinking behavior (actual descriptive norms), have demonstrated considerable success in reducing normative perceptions and alcohol consumption in college student populations (S. E. Collins, Carey, & Sliwinsky, 2002; Cunningham, Humphreys, & Koski-Jännes, 2000; Lewis & Neighbors, 2006a; Murphy et al., 2004; Neighbors et al., 2004; Walters, 2000). In fact, several trials support the efficacy of stand-alone PNF interventions (for a review, see Zisserson, Palfai, & Saitz, 2007), which have evidenced similar effect sizes compared with PNF delivered as part of multicomponent interventions (Walters & Neighbors, 2005).
Despite the growing body of evidence in support of PNF-only interventions, questions remain regarding what level of specificity of referent is most effective. Moreover, limited data exists assessing the utility of web-based PNF outside of the laboratory, but the limited results suggest that this approach can lead to reductions in alcohol consumption (Bewick et al., 2010; Neighbors, Lewis, et al., 2010; Walters, Vader, & Harris, 2007). Web-based PNF has the potential to provide a cost-effective, standardized intervention that can be easily disseminated to large groups, while being appealing to college students who perceive this modality to be unobtrusive and convenient (Neighbors, Lewis, et al., 2010; Riper et al., 2009). In the current study, we aimed to address this gap in the literature by examining the efficacy of web-based PNF using varying levels of reference group specificity.
Specificity of Normative Reference GroupThe majority of PNF initiatives have used typical student-normative referents (S. E. Collins et al., 2002; Murphy et al., 2004; Neighbors et al., 2004; Walters, 2000). However, recent research has indicated that increasing the specificity of normative reference groups (e.g., gender specific) may enhance the efficacy of PNF interventions for certain individuals (Lewis & Neighbors, 2006a). This is consistent with theoretical perspectives that suggest more socially proximal and salient, as compared with more distal, social reference groups have a greater impact on an individual’s behavioral decisions (e.g., social comparison theory, Festinger, 1954; social impact theory, Latané, 1981). Indeed, researchers have found descriptive norms for more socially proximal referents (e.g., close friends, same-sex students) tend to be more strongly associated with alcohol consumption than those of “typical” or “average” students (Korcuska & Thombs, 2003; Lewis & Neighbors, 2004; Lewis, Neighbors, Oster-Aaland, Kirkeby, & Larimer, 2007). Furthermore, Larimer et al. (2009, 2011) reported that even at increasing levels of specificity (i.e., gender, ethnicity, residence), students overestimated descriptive-normative drinking behaviors of proximal peers, and these misperceptions were uniquely related to personal drinking. Targeting more specific reference groups may be particularly effective in communicating feedback that closely resembles the individual respondent, thereby increasing the saliency, believability, and recognition of the information presented and, in turn, more strongly promoting positive behavioral change. In the current study, we focused on normative reference groups derived from combinations of participants’ gender, race, and Greek status.
Gender and Greek Specificity
Gender and Greek status are two levels of specificity that may influence the impact of PNF interventions. Men and women exhibit different drinking behaviors (Kypri, Langley, & Stephenson, 2005) and perceptions of normative beliefs (Lewis & Neighbors, 2004, 2006a; Suls & Green, 2003). Efficacy studies of gender-specific PNF have revealed inconsistent results. For example, Lewis and Neighbors (2007) did not find any overall differences in the short-term efficacy of gender-specific and nongender-specific PNF, although both groups reported reductions in drinking compared with controls. However, gender-specific feedback worked better for women who identified more closely with their gender. Neighbors, Lewis et al. (2010) demonstrated PNF delivered biannually with gender-specific norms reduced weekly drinking, whereas nongender-specific and one-time only gender-specific norms did not.
Students affiliated with fraternities and sororities (Greek systems) hold significantly higher perceived and actual drinking norms (Carter & Kahnweiler, 2000) than non-Greek peers. Larimer et al. (2011) found Greek students’ perceived norms for referents that do not include Greek status tended to be close to, if not lower than, their own drinking behavior. However, Greek students presented with referents that did include Greek status overestimated normative drinking. As such, Greek-specific normative feedback may be particularly beneficial to Greeks, who appear amenable to normative feedback interventions (LaBrie et al., 2008; Larimer et al., 2001; Larimer, Turner, Mallett, & Geisner, 2004).
Ethnicity/Race Specificity
Currently, no studies have addressed the efficacy of race-specific PNF, and limited data are available examining racial and ethnic differences in norms and their relationship to alcohol use (LaBrie, Atkins, Neighbors, Mirza, & Larimer, 2012). The few studies in which ethnic- and race-specific reference groups have been examined suggest perceived norms vary on the basis of the race specificity of the reference group (Larimer et al., 2009, 2011), and perceived norms for same-ethnicity students are positively associated with drinking, particularly for those who identify most strongly with their ethnic group (Neighbors, LaBrie, et al., 2010). The typical American college student is most often viewed as Caucasian, even among non-Caucasian students (Lewis & Neighbors, 2006b), thus perceived typical student norms may be less predictive of drinking among non-Caucasian students. Indeed, Stappenbeck, Quinn, Wetherill, and Fromme (2010) found that although Caucasian and Asian students do not differ in perceived typical student norms, generic norms were predictive of alcohol use and own social group norms for Caucasian, but not Asian students.
Taken together, these findings suggest that PNF interventions may benefit from providing race-specific feedback. In the current study, we extend previous research by examining the impact of race-specific PNF among Caucasians students, the prototypical heavy-drinking racial subgroup in college populations, and Asian American students. Although Asian Americans have higher rates of abstinence than other ethnic groups, Asian American adolescents who do drink have higher rates of binge drinking than any other ethnic group, and this racial subgroup exhibits escalating rates of heavy episodic drinking and alcohol abuse (Grant et al., 2004; Hahm, Lahiff, & Guterman, 2004; Office of Applied Studies, 2008; Wechsler, Dowdall, Maenner, Gledhill-Hoyt, & Lee, 1998; Wechsler et al., 2002). These findings have led to calls for alcohol prevention efforts to specifically target this ethnic minority (e.g., Hahm et al., 2004; LaBrie, Lac, Kenney, & Mirza, 2011). Thus, in the current study we focused on Asian students in order to contribute to prevention efforts for this understudied group as well as to extend work of Stappenbeck and colleagues (2010) to evaluate the efficacy of typical student versus ethnic-specific feedback for diverse populations. We also selected Asian students, as they represent an ethnic minority population of sufficient size and with distinct drinking behavior and norms from the majority population to enable a strong test of our research questions.
Discrepancy of Actual Norm With Behavior and PerceptionsPNF approaches correct normative misperceptions by showing discrepancies between actual norms and students’ perceptions and behaviors in order to motivate behavior change (Rice, 2007). Presumably, for PNF to be effective, inaccurate beliefs must be present (Lewis & Neighbors, 2006a), and the greater the discrepancy between actual norms and perceived norms, and actual norms and behavior, the greater the potential impact of normative feedback (Larimer et al., 2004). Larimer et al. (2011) examined the accuracy of students’ perceived norms using reference groups varying in similarity to the participant, including typical student and combinations of gender, race, and Greek status. Participants rated the referent to have higher levels of alcohol consumption relative to their own drinking, and, in general, as the referent became more similar, mean normative estimates generally decreased. Thus, the greatest discrepancy between perceived norms and actual norms occurred when the typical student referent was used. Although students may find specific normative information more relevant, compelling, and, therefore, motivating, the greater accuracy of descriptive norms for specific reference groups may reduce the discrepancy and decrease the motivating potential of normative feedback. In the current study, we examine whether intervention effects are mediated by discrepancies between actual norms, perceived norms, and drinking behavior.
The Current StudyWe compare the efficacy of web-based PNF using one of eight increasingly specific reference groups (typical student and gender-, race-, Greek status-, gender-race-, gender-Greek status-, race-Greek status-, gender-race-Greek status-specific) compared against a web-based motivational feedback intervention derived from the well-established BASICS intervention (Brief Alcohol Screening and Intervention for College Students; Dimeff, Baer, Kivlahan, & Marlatt, 1999) and a generic feedback control in the current study. The Web-BASICS control provides an opportunity to examine whether addition of comprehensive feedback components offers any advantages over stand-alone PNF, whereas the generic control condition allows us to examine whether completing alcohol-related questionnaires and receiving nonalcohol-related feedback could be responsible for intervention effects. We hypothesized that both PNF and Web-BASICS would outperform an assessment-only control condition in reducing risky drinking (number of weekly drinks, peak drinks in the past month, and days of drinking during the past month) and negative consequences of alcohol use. We further predicted that increasing levels of specificity of feedback would be more effective in reducing risky drinking and consequences such that PNF with three levels of specificity (same-sex, same-race, same Greek membership status) would outperform two and one levels and typical student feedback. Finally, we examined the role of discrepancy between drinking behavior, perceived descriptive norms, and the actual drinking norm for each reference group as a mechanism of intervention efficacy.
Method Participants and Procedure
Participants were undergraduate students from two West Coast universities. A random list of enrolled students (N = 11,069; n1 = 6,495; n2= 4,574) was provided by the registrar’s office. Students were contacted via mail and e-mail to participate in an online screening survey. Of participants contacted, 4,818 (43.5%) responded and completed the screening survey (60.2% female). Campus 1 (n1 = 3,034), a large, public university, has an enrollment of approximately 30,000 undergraduate students. Campus 2 (n2 = 1,784) is a mid-sized private university with enrollment of approximately 6,000 undergraduates. Participants were between 18 and 24 years old (M = 19.86, SD = 1.35). Racial composition was 50.7% Caucasian, 27.4% Asian, 10.7% multiracial, 6.4% “other,” 2.5% African American, 1.6% Hawaiian/Pacific Islander, and 0.5% American Indian/Alaskan Native. Furthermore, 10.9% self-identified as Hispanic. The screening samples were similar to the college populations from which they were drawn with respect to alcohol use. For example, a similar proportion of students reported that they did not drink on a typical week (Campus 1: 35.2% screening, 37.2% population; Campus 2: 25.7% screening, 27.7% population). In terms of demographics, females were slightly overrepresented in the screening sample (Campus 1: 56.7% screening, 51.6% population; Campus 2: 65.6% screening, 57.9% population), and White students were underrepresented in the Campus 1 sample (Campus 1: 45.7% screening, 56.6% population; Campus 2: 59.1% screening, 55.5% population).
A total of 2,034 (42.2%) out of the 4,818 students who completed the screening survey met inclusion criteria for the current study. Inclusion criteria consisted of participants reporting a minimum of one past-month heavy episodic drinking event (HED; consuming at least four [for female] or five [for males] drinks during a drinking occasion) and identifying as either Caucasian or Asian. Of the 2,034 participants who met inclusion criteria, 1,831 (90%) students completed an online baseline survey, and 1,663 were randomized to one of the 10 conditions reported on in this article. Another condition (n = 168) was a minimal assessment control condition comprising students who did not participate in the 1-, 3- or 6-month follow-up periods and therefore was not included in the current analysis. Follow-up rates were 89.7% at 1 month, 86.8% at 3 months, 84% at 6 months, and 85.5% at 12 months. The final sample was 56.7% female, with a mean age of 19.92 years (SD = 1.3). The majority of the sample identified as Caucasian (75.7%) and did not belong to a sorority or fraternity (70.7%).
Study Design
The current study was approved by the Institutional Review Boards of both participating universities, and a Federal Certificate of Confidentiality was obtained to further protect research participants.
Screening
Students randomly selected from registrar rosters at both universities received mailed and e-mailed letters inviting their participation in a study of alcohol use and perceptions of drinking in college. The invitations included a URL to a 20-min online screening survey, which gathered demographic, alcohol use, and descriptive and injunctive norms data. Screening survey completers received a $15 stipend.
Baseline
Students completing the screening survey who met inclusion criteria were immediately invited to participate in the longitudinal trial. Students were presented with a web invitation, which provided a URL directing them to the baseline survey. The baseline survey included additional measures related to study hypotheses such as an assessment of negative consequences of drinking. Baseline survey completers received a $25 stipend. Upon completion of the baseline survey, students were randomly assigned to one of the 10 treatment conditions using a web-based algorithm. A stratified, block randomization was used (Hedden, Woolson, & Malcolm, 2006), in which assignment was stratified by Greek organization membership (yes/no), sex (male/female), race (Asian/Caucasian), and total drinks per week (10 or fewer, 11 or more). Thus, each treatment condition was composed of approximately 82 men and 100 women, 43 Asian Americans and 139 Caucasians, and 55 Greek-affiliated students and 127 non-Greek students.
PNF intervention
Of the 10 conditions examined in the current study, eight provided normative feedback based on differing levels of specificity of the reference group. Condition 1 was provided normative information about the typical student at the same university. Conditions 2–4 were provided matched normative information at one level of specificity based on the participant’s gender, Greek status, or race. Conditions 5–7 were presented two levels of specificity for students at the same university matched to participant’s gender and race (e.g., typical female Asian), gender and Greek status (e.g., typical male Greek-affiliated student), or race and Greek status (e.g., typical Caucasian Greek-affiliated student). The eighth condition provided participants with three levels of specificity for students at the same university matched to participant’s gender, race, and Greek status (e.g., typical female, Asian, Greek-affiliated student). A ninth condition presented Web-BASICS (Dimeff et al., 1999). Finally, the 10th condition was a repeated assessment control group who received generic nonalcohol-related normative feedback about the typical student’s frequency of text messaging, downloading music, and playing video games on their campus.
After completing the baseline survey, participants were immediately provided with Web-based feedback, depending on their randomized condition. Three feedback categories were used: PNF (Conditions 1–8 described above), Web-BASICS (Condition 9), and generic control feedback (Condition 10). Participants were given the option to print their feedback.
The PNF
The PNF contained four pages of information in text and bar graph format. Separate graphs, each including three bars, were used to present information regarding the number of drinking days per week, average drinks per occasion, and total average drinks per week for (a) one’s own drinking behavior, (b) their reported perceptions of the reference group’s drinking behavior on their respective campus, at the level of specificity defined by their assigned intervention condition, and (c) actual college student drinking norms for the specified reference group. Actual norms were derived from large representative surveys conducted on each campus in the prior year as a formative step in the trial. Participants were also provided with their percentile rank comparing them with other students on their respective campus for the specified reference group (e.g., “Your percentile rank is 99%; this means that you drink as much or more than 99% of other college students on your campus”).
Web-BASICS feedback
The Web-BASICS feedback contained a total of 26 pages of interactive comprehensive motivational information based on assessment results, modeled from the efficacious in-person BASICS intervention (Dimeff et al., 1999; Larimer et al., 2001). It addressed quantity and frequency of alcohol use; past-month peak alcohol consumption; estimated blood alcohol content (BAC); and provided information regarding standard drink size, how alcohol affects men and women differently, oxidation, alcohol effects, reported alcohol-related experiences, estimated calories, and financial costs based on reported weekly use, estimated level of tolerance, risks based on family history, risks for alcohol problems, and tips for reducing risks while drinking as well as alternatives to drinking. The feedback also included PNF using typical student drinking norms. Participants were given the option to click links throughout the feedback to obtain additional information on standard drink size, sex differences and alcohol use, oxidation, biphasic tips, hangovers, alcohol costs, tolerance, and protective factors, as well as provided with a link to a BAC calculator.
Generic control feedback
The generic control feedback, which was presented to those in the assessment control condition, contained three pages of information in text and bar graph format. Separate graphs, each including two bars, were used to present information regarding the number of hours spent texting, number of hours spent downloading music, and number of hours spent playing video games per week for (a) one’s own behavior and (b) actual college student behavior. Participants were also provided with their percentile rank comparing them with other students on their respective campus (e.g., “Your percentile rank is 60%; this means that you text as much or more than 60% of other college students on your campus”).
Follow-up
To assess intervention efficacy, participants were invited to take a series of online follow-up surveys at 1-, 3-, 6-, and 12-month time points after their online intervention. Participants received $30 for completing the 1-, 3- and 6-month follow-up surveys and $40 for completing the 12-month follow-up survey. Additionally, students who completed all surveys received a bonus check of $30 at the end of the study.
Measures
All measures were completed at screening/baseline, 1-, 3-, 6-, and 12-month follow-up. A standard drink definition was included for all alcohol consumption measures (i.e., 12 oz. beer, 10 oz. wine cooler, 4 oz. wine, 1 oz. 100 proof [1 [1/4] oz. 80 proof] liquor).
Demographics
The initial section of the screening survey asked participants to report their birth sex, race, and Greek status.
Alcohol consumption
The Daily Drinking Questionnaire (DDQ; R. L. Collins, Parks, & Marlatt, 1985; Kivlahan, Marlatt, Fromme, Coppel, & Williams, 1990) measured one of the primary outcomes: the number of drinks per week. Students were asked to consider a typical week in the last month and indicate the number of drinks they typically consumed on each day of the week. Students’ responses were summed across each of the 7 days to form a composite of total weekly drinks.
The Quantity/Frequency Index is an assessment of alcohol use (Baer, 1993) that measures participant’s drinking during the past month. Participants were asked to think about the occasion when they drank the most and to report how many drinks they consumed on that occasion. In addition, participants reported how many days they drank alcohol in the past month. Response options ranged from 0 (I do not drink at all) to 7 (Every day).
Descriptive norms
The Drinking Norms Rating Form (DNRF; Baer, Stacy, & Larimer, 1991) assessed participants’ perception of the number of drinks consumed each day of the week by a typical student at one’s university and at varying levels of reference group specificity. The levels of specificity referred to a typical student’s gender, race, and Greek status and all combinations of the tree, resulting in eight reference groups for each question.
Alcohol-related negative consequences
The 25-item Rutgers Alcohol Problem Index (RAPI; White & Labouvie, 1989) assessed the frequency of alcohol-related negative consequences. Response options ranged from 0 (never) to 4 (10 or more times). The items included “Passed out or fainted suddenly”; “Caused shame or embarrassment to someone”; and “Felt physically or psychologically dependent on alcohol.” Items were summed to create a composite score for the analysis.
Data Analyses
The first two hypotheses examining the efficacy of PNF compared with Web-BASICS and control conditions, and the efficacy of PNF conditions varying in specificity of feedback, were tested using a quasi-Poisson generalized linear model fit by generalized estimating equations (GEE; Liang & Zeger, 1986). The primary outcomes included number of drinks consumed per week, peak drinks in the past month, drinking days during the past month, and total number of alcohol-related problems. Each of these outcomes represents a type of count variable. Count variables have certain properties (e.g., bounded at zero, integer scaling) that make them ill-suited for statistical methods that assume normality and are more appropriately modeled by count regression methods (see Atkins & Gallop, 2007). Poisson GEE models are appropriate for clustered or longitudinal count data and control for correlated data through estimating a working correlation matrix of the residuals and using robust, cluster-adjusted standard errors. However, the basic Poisson GEE assumes that the mean of the outcome is equal to its variance (conditional on the covariates). This is often violated in real-world data, leading to a condition called overdispersion, which yields biased standard errors and statistical tests (Hilbe, 2011). The quasi-Poisson GEE is a semiparametric mean model that incorporates an overdispersion parameter, yielding unbiased variance estimates in the presence of overdispersion.
The predictors were connected to the outcome through a natural logarithm link function, which is the standard link function for Poisson models and other count regression methods. To interpret quasi-Poisson regression models, the coefficients are typically exponentiated (i.e., eB) to yield rate ratios (RRs). Like odds ratios in logistic regression, a value of 1 is a null value for RRs (i.e., no effect), and RRs larger than 1 are interpreted as a percentage increase in counts (for each unit increase in the predictor). Conversely, RRs less than 1 are interpreted as percentage decreases in the outcome (for each unit decrease in the predictor).
The basic quasi-Poisson model used to test primary hypotheses (using the DDQ outcome as an example) was:
As seen in Equation 1, the baseline level of the outcome was included as a covariate in all analyses, which increases the efficiency of the model (i.e., reduces SE for treatment contrasts and other terms), and a participant’s outcome in the regression models included his or her values of the outcome at 1, 3, 6, and 12 months postbaseline. Time was modeled as a linear association with outcomes, which was confirmed through sensitivity analyses that allowed more flexible, nonlinear associations. In Equation 1, a single treatment indicator is shown (Tx), but in analyses this was replaced by appropriate treatment contrasts, described below in the Results section. Randomization excluded the possibility of baseline confounders, and there were no concerns about treatment comparability at baseline. Hence, models did not adjust for additional covariates. The proportion of missing data were consistent across treatment conditions (see Figure 1), and sensitivity analysis demonstrated no differences based on missing data status. A priori power analyses given the current design indicated that treatment condition sample sizes of n = 141 or greater (accounting for planned attrition of 20%) would yield power of .80 or better to detect treatment contrasts of d = 0.20 (e.g., small effect sizes). All analyses were done in R v2.11.1 (R Development Core Team, 2010).
Figure 1. Participant flow through the study. BASICS = Brief Alcohol Screeing and Intervention for College Students; m = month.
Results Descriptive Analyses
Participants in the randomized control trial sample reported consuming an average of 11.03 drinks (SD = 9.5; males M = 14.23, SD = 11.5; females M = 8.58, SD = 6.6) in a typical week. Furthermore, on the occasion on which participants drank the most in the past month, they reported drinking an average of 8.77 drinks (SD = 4.1; males M = 10.68, SD = 4.3; females M = 7.31, SD = 3.2) on a single occasion. Table 1 has descriptive statistics for each of the 10 treatment conditions (i.e., all PNF conditions are reported separately).
Mean Drinking and Consequences Outcomes for Different Treatment Groups at Five Time Points
Quasi-Poisson GEE Analyses of Control Versus Web-BASICS Versus PNF
Initial inferential statistics focused on treatment comparisons between control, Web-BASICS, and PNF (considered as a single group). As noted earlier, a quasi-Poisson GEE model was fit that included time, indicator variables for Web-BASICS and PNF (compared with control), and the outcome measured at baseline. A model including interactions between time and treatment conditions was examined. These interactions were not significant, indicating that all change occurred from baseline to 1 month with little change following that, and thus the simpler model was retained, including main effects for treatment and time. Moderation of intervention effects by demographic variable (e.g., race, gender, and Greek membership) was also nonsignificant. RRs and 95% confidence intervals (CIs) for RRs are shown in Table 2.
Rate Ratios (RRs) and 95% Confidence Intervals (CIs) for RR From Quasi-Poisson GEE Comparing Control, BASICS, and PNF Participants From 1 to 12 Months
Focusing on total weekly drinking, the intercept term is the estimate of drinking for control participants at baseline (i.e., time = 0) because of the coding of the indicator variables. The RR for that term provides the average outcome for this group (e.g., the mean total drinks per week is 8.7 in the control group). The effect for time presents the adjusted common change across time (i.e., there is approximately a 0.6% decrease in drinks per week every month postintervention in the control group). Compared with the control group, PNF participants showed a 4% reduction in average total drinks, significantly lower than control participants. The Web-BASICS and control conditions were not significantly different from one another. Findings for the other three outcomes are broadly similar: PNF participants reported significantly less peak drinking, drinking days, and drinking-related problems (RAPI) relative to control participants. However, in each case, the differences are modest (between 1% and 8%). Web-BASICS participants reported significantly lower peak drinking and total drinking days relative to control, but RAPI scores were similar between the two groups. Finally, contrasts examined whether there were differences between the two active treatment groups (combined PNF = 0, Web-BASICS = 1), and they revealed no significant differences for total weekly drinks (RR = 0.96, 95% CI [0.85, 1.09]), peak drinks (RR = 1.01, 95% CI [0.94, 1.10]), total drinking days (RR = 1.00, 95% CI [0.91, 1.09]), and drinking-related problems (RR = 0.91, 95% CI [0.67, 1.25]).
Quasi-Poisson GEE of Individual PNF Conditions
We next examined whether a more specific comparison group with PNF might yield better treatment outcomes relative to a generic “typical” student comparison group. Using only the eight PNF conditions, a quasi-Poisson GEE examined whether greater specificity in the normative reference group would lead to greater reductions in drinking. Table 3 presents RR and 95% CI for RR for comparisons among PNF conditions. No PNF condition led to greater change over time in any of the four outcomes as compared with typical student feedback. Surprisingly, just the opposite was found: All RRs comparing more specific PNF references with the typical student were greater than 1, and virtually all were significant. Thus, more specific PNF conditions achieved reliably worse results compared with typical student feedback.
Rate Ratios (RRs) and 95% Confidence Intervals (CIs) for RR From Quasi-Poisson GEE Comparing Typical Student PNF With More Specific PNF Conditions From 1 to 12 Months
Treatment Mediators and Mechanisms
Several analyses examined possible mediators or mechanisms for why typical student feedback might be superior to feedback with more specific reference groups, focusing on total drinks per week during follow-up, as in the earlier PNF-focused treatment analyses. The perceived descriptive drinking norm (measured as the average rating on the DNRF across reference groups at each time) was considered as a mediator of treatment efficacy. The approach to mediation was similar to the classic approach to mediation, in which a total effect of treatment is decomposed into a direct effect of treatment and indirect effect through the mediator. However, we used a bootstrapped, nonparametric method for estimating the quantities (Imai, Keele, & Tingley, 2010). Table 4 shows results for mediation analyses, comparing each of the other seven PNF conditions with typical student PNF. The total effect column reports the estimated mean difference in total weekly drinks between typical student PNF and the specified treatment condition (i.e., basic treatment difference expressed as estimated mean difference), and the indirect effect column reports the amount of the total effect that can be explained by the indirect pathway through the DNRF. These results show that changes in the DNRF account for 11%–51% of the treatment superiority of the typical student PNF relative to other PNF conditions.
Mediation Results for DNRF and Two Different Types of Discrepancy
The putative mechanisms of PNF include the discrepancy between the individual’s own drinking and the actual descriptive drinking norm they are provided during the feedback, as well as the discrepancy between their perception of the norm (i.e., DNRF) and the norm provided during feedback. Conceptually, we consider these to be treatment mechanisms, as opposed to mediators or moderators. They are not moderators as they are directly manipulated as part of the treatment, but they are also not traditional mediators because the treatment does not influence the discrepancy, but rather the discrepancy is part of the intervention itself. However, if the pragmatic goal is to separate the effect of treatment content (i.e., discrepancy) and treatment type, then analytically, we can consider discrepancy as a mediator to achieve this aim. Results are shown in Table 4.
Approximately 5% of the total effect (i.e., estimated mean treatment difference) can be explained by the discrepancy with one’s own drinking and even less by the discrepancy with perceived norm. Thus, relative to the DNRF as a mediator, these treatment mechanisms appear to be somewhat weaker explanations for the treatment difference. Considering the indirect effect as a percentage change in the treatment difference, there is a 2.9% (CI [2.4%–3.5%], p < .001) change in total drinks per week with each unit change in DNRF, a 14% (CI [12%–18%], p < .001) change in total drinks per week with each unit change in discrepancy with one’s own drinking, and a 0.4% (CI [0.0%–0.9%], p = .57) change in total drinks per week with each unit change in discrepancy with perceived norm. Thus, in understanding the difference between typical student feedback versus more specific PNF, both DNRF and discrepancy with own drinking appear to significantly affect the treatment differences. In summary, typical student PNF appears to yield greater changes in typical weekly drinking in part by having greater influence on perceptions of descriptive norms (i.e., DNRF) as well as generating a larger discrepancy with the student’s own drinking relative to other PNF conditions.
DiscussionIn the current study, we evaluated the efficacy of web-based PNF in reducing drinking and alcohol-related negative consequences relative to an active comparison condition (Web-BASICS) and a control condition. Relative to the control condition, PNF (considered as a single group) was associated with reductions in each of the four outcomes (number of weekly drinks, peak drinks, days of drinking, and number of alcohol-related problems). However, the effects of the web-based PNF were modest, with reductions ranging from a 1.0% decrease in number of drinking days to an 8.1% decrease in maximum number of drinks consumed on one occasion. PNF appeared to have more of an effect on the amount students drank (total drinks and peak drinks) than on drinking frequency. Furthermore, compared with control, Web-BASICS was associated with a decrease in number of drinking days (2.5%) and peak number of drinks, but no change in number of alcohol-related negative consequences at the 12-month assessment.
Findings also indicated that the PNF (when considered as a single group) and Web-BASICS interventions did not differ significantly from each other, which suggests that a brief web-based PNF intervention with a focus only on normative comparisons is as efficacious as a more inclusive Web-BASICS intervention that focuses on normative comparisons in addition to a wide range of other feedback components (e.g., blood alcohol, content, expectancies, protective behavioral strategies). Because both interventions were comparable at 12 months, a more parsimonious PNF intervention might be a preference over a more inclusive BASICS intervention, at least with respect to web-based interventions. It is worth noting that Web-BASICS includes PNF feedback. The absence of differences may suggest that components within Web-BASICS other than PNF (e.g., expectancy information, review of risk factors, review of consequences experienced) may not offer unique impact over and above PNF.
We also extended existing research in the current study by examining the influence of specificity of normative referent group on the efficacy of web-based PNF. In contrast to expectations, the PNF intervention was most effective when the typical student (i.e., least specific normative referent) was used as the normative reference group. Thus, students who engaged in HED and were given personalized information highlighting the discrepancy between their own drinking behavior, their perception of typical student drinking norms, and the actual drinking behavior of the typical student reduced their drinking more and experienced fewer negative consequences than when they were given personalized information relative to the drinking behavior of more specific normative referent groups. For example, on average, heavy-drinking Asian men and Greek women reduced their drinking more when they were compared with the typical student rather than with the typical Asian male student or the typical Greek female student.
Mediation analyses indicated that typical student PNF was associated with greater changes in typical weekly drinking, in part, by having a stronger influence on descriptive normative perceptions. Thus, typical student PNF resulted in a greater discrepancy with a student’s own drinking relative to other PNF conditions. One plausible explanation as to why PNF that used the typical student-normative referent was more efficacious is that participants may be more likely to project characteristics that they felt were important or that generalize to a drinking college student onto the nondescriptive typical student referent rather than having those characteristics selected for them. Previous research has shown that students often perceive the typical student as different from themselves (Lewis & Neighbors, 2006b; e.g., the typical student is perceived as male and Caucasian). Greater discrepancies may arise from students’ inability to fully envision or define the “typical student.” Along with projecting characteristics onto this blank slate that may be important to the individual, students may also find it easy to project the highly salient and prototypical behavior of a heavy-drinking college student (hence, the largest perceived norms for this group). In this way, the discrepancy becomes larger, as does the relative importance of the typical student. Students may be more likely to think about how their drinking relates to other students in general rather than to other students who share their specific demographic characteristics. The combination of the two projection effects may result in more compelling feedback, thus promoting greater cognitive dissonance between perceived norms, actual norms, and an individual’s own behavior. Under the tenets of social norms theory, this dissonance would produce greater change. In contrast, students’ schema for drinking norms may not extend to very specific subgroups, and the additional complexity of proximally specific reference information may undermine the otherwise straightforward message conveyed by PNF. Students may feel more confident in their knowledge of the drinking norms of more specific groups, and the lack of a large discrepancy may further reduce the potential for change despite what is theoretically purported to be more meaningful and influential feedback.
Although typical student PNF outperformed more specific PNF conditions in this trial, this does not rule out the importance of considering group characteristics or social identity in the context of norms-based interventions. Perhaps if the feedback highlighted the salience of the participant’s membership to the more relevant referent group, it would have been more efficacious compared with PNF about the typical student. More specific reference groups may be more influential only when they are also accompanied by identification with those groups. Recent studies have shown that the association between perceived norms for specific reference groups and drinking behavior is moderated by degree of identification with, or feelings of connectedness to, the group in question (Hummer, LaBrie, & Pedersen, 2012; Neighbors, LaBrie, et al., 2010; Reed, Lange, Ketchie, & Clapp, 2007). There is considerable variability in the extent to which individuals identify with others who share their demographic characteristics. Furthermore, individuals may identify strongly with one or two demographic dimensions and not at all with others. For example, an Asian sorority woman may strongly identify with her gender and sorority but not her race, or with her race but not her gender or sorority. Thus, specificity of the reference group in normative feedback may only matter to the extent that it is matched to group identification. This explanation is consistent with Lewis and Neighbors’ (2007) study in which gender-specific normative feedback was only more effective than gender nonspecific-normative feedback for women who identified more strongly with their gender. Additional research is needed to evaluate the efficacy of self-defined important normative referents.
Another consideration is the feedback in this study was provided remotely on the web. Previous studies have demonstrated larger effects of computer-based PNF when students are required to come in to the lab (Neighbors, Lewis, et al., 2010). This may be due in part to competing demands for attentional resources while students consider estimates for drinking norms and/or review PNF. Students may pay less attention while completing web-based interventions; they may be simultaneously talking on the phone, texting, watching television, and the like, whereas they would be less likely to engage in distracting activities in a lab-based intervention. If the influence of specificity of norms feedback requires more attention to the material, then we would expect a greater likelihood of effects in a more controlled setting.
Regardless of why we did not find strong effects for PNF that used more specific normative referents, the current findings suggest that web-based PNF that uses the typical student referent group may be an optimal choice and has the added advantage of being more parsimonious for college personnel in collecting norms and designing feedback interventions.
Clinical Implications
In the current study, both Web-BASICS and PNF interventions delivered via the Web are associated with reduced drinking through 12-month follow-up, and PNF is also associated with reduced negative consequences. Although these reductions are relatively small in magnitude, from a public health perspective, the very low-cost and easy-to-implement typical student PNF is associated with sustained reductions and therefore has broad potential for large-scale implementation. This intervention can be implemented with comparable or fewer resources than are currently used for educational or awareness campaigns shown to be ineffective for college drinking prevention (Cronce & Larimer, 2011; Larimer & Cronce, 2007). In the current study, there was no significant advantage of the more comprehensive Web-BASICS intervention relative to PNF alone, providing additional evidence that more is not necessarily better (Kulesza, Apperson, Larimer, & Copeland, 2010; Wutzke, Conigrave, Saunders, & Hall, 2002). More research is needed to evaluate potential moderators of efficacy of Web-BASICS and PNF, as well as moderators of more specific versus less specific PNF feedback efficacy. Nonetheless, the current findings are encouraging and provide further evidence that a low-cost, low-complexity PNF intervention can demonstrate lasting effects on student drinking.
Limitations and Future Directions
This study is not without limitations. One of the most notable limitations of this study is that we defined the specificity of the normative referent group in order to increase relevance to that group. It may be that students did not care or identify with a more specific normative referent group as we defined it. Future research should evaluate whether PNF using self-defined normative referents is more efficacious than PNF using researcher-defined normative referents. For example, it would be interesting to also ask students to generate a list of groups with which they most strongly identify. It would also be feasible to allow students to select from a set of possible reference groups with whom their drinking might be compared. An additional limitation is that the current study was limited to Caucasians and Asians. It is unknown whether findings would generalize to other racial/ethnic groups. Finally, we only evaluated in the current study specificity of the normative referent group relative to descriptive drinking norms. Future research is necessary to evaluate whether more specific normative referent groups are more effective than less specific normative referent groups when presenting feedback based on injunctive drinking norms.
ConclusionsThe current research extends previous implementation of social norms-based interventions for drinking in several ways. This is the first study to evaluate PNF on the basis of specifying the normative referent in regards to race, gender, and Greek status, and to test a PNF intervention with a large sample of Asian ethnic minority students engaged in HED. Asian students, as noted previously, are a growing risk group for heavy drinking and alcohol use disorders (Grant et al., 2004; Hahm et al., 2004; Office of Applied Studies, 2008; Wecshler et al., 2002), and are often underrepresented in alcohol research trials. Furthermore, the study directly tests the extent to which increasing specificity of the reference group across multiple dimensions of demographic similarity improves (or fails to improve) efficacy of PNF, and tests the magnitude of the normative discrepancy as a potential mechanism explaining the advantage we found for typical student PNF in this context. This has both theoretical and practical significance, as it addresses a critical tension in the normative feedback literature between ostensibly enhancing relevance of the feedback through a focus on highly proximal/similar reference group norms versus emphasizing the largest normative discrepancy, which is generally represented by the typical student norm. Furthermore, although typical student norms are often readily available through annual or routine campus alcohol-related surveys, more specific normative information may be less readily available and entail considerable expense to collect. Thus, the benefit of using typical student-normative feedback demonstrated in the current study has important implications for implementation of PNF interventions on college campuses. Additionally, this is the first study to evaluate a direct comparison between PNF and Web-BASICS. Inclusion of two meaningful comparison groups, the Web-BASICS condition and the nonalcohol feedback control condition, increases our understanding of the extent to which typical student PNF is an efficacious and parsimonious approach to reducing alcohol use among ethnic majority and Asian minority students, as well as both males and females and those in Greek organizations. This research extends a growing literature emphasizing the importance of normative comparisons in constructing brief single and multicomponent interventions aimed to reduce drinking. The research further contributes to a small body of studies challenging the conventional wisdom that more comprehensive interventions are superior to minimal interventions in producing drinking reductions. We expect the current study will stimulate additional research in these areas. On the basis of these and previous findings, we would encourage the use of the typical normative referent group when constructing PNF for students identified as heavier drinkers and web-based delivery of feedback.
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Submitted: August 3, 2012 Revised: June 18, 2013 Accepted: July 8, 2013
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Source: Journal of Consulting and Clinical Psychology. Vol. 81. (6), Dec, 2013 pp. 1074-1086)
Accession Number: 2013-28918-001
Digital Object Identifier: 10.1037/a0034087
Record: 45- Title:
- Self-criticism and dependency in female adolescents: Prediction of first onsets and disentangling the relationships between personality, stressful life events, and internalizing psychopathology.
- Authors:
- Kopala-Sibley, Daniel C.. Department of Psychology, Stony Brook University, Stony Brook, NY, US, daniel.kopala-sibley@stonybrook.edu
Klein, Daniel N.. Department of Psychology, Stony Brook University, Stony Brook, NY, US
Perlman, Greg. Department of Psychiatry, Stony Brook University, Stony Brook, NY, US
Kotov, Roman. Department of Psychiatry, Stony Brook University, Stony Brook, NY, US - Address:
- Kopala-Sibley, Daniel C., Department of Psychology, Stony Brook University, Stony Brook, NY, US, 11794-2500, daniel.kopala-sibley@stonybrook.edu
- Source:
- Journal of Abnormal Psychology, Vol 126(8), Nov, 2017. pp. 1029-1043.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 15
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- personality, self-criticism, dependency, anxiety, depression
- Abstract (English):
- There is substantial evidence that personality traits, such as self-criticism and dependency, predict the development of depression and anxiety symptoms, as well as depressive episodes. However, it is unknown whether self-criticism and dependency predict the first onset of depressive and anxiety disorders, and unclear how to characterize dynamic mechanisms by which these traits, stressful life events, and psychopathology influence one another over time. In this study, 550 female adolescents were assessed at baseline, 528 and 513 of whom were assessed again at Waves 2 and 3, respectively, over the course of 18 months. Self-criticism and dependency were assessed with self-report inventories, depressive and anxiety disorders were assessed with diagnostic interviews, and stressful life events were assessed via semistructured interview. Logistic regression analyses showed that self-criticism and dependency significantly predicted the first onset of nearly all depressive and anxiety disorders (significant polychoric rs ranged from .15–.42). Subsequent path analyses focused on prediction of depression, and supported several conceptual models of personality-stress-psychopathology relationships. In particular, Personality × Stress interactions were evident for both dependency and self-criticism. These interactions took the form of dual vulnerability, such that stressful life events predicted an increased probability of a later depressive disorder only at low levels of each trait. Results suggest the traits of self-criticism and dependency are important to consider in understanding who is at risk for depressive and anxiety disorders. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Impact Statement:
- General Scientific Summary—This study examined whether the personality traits of self-criticism and dependency predict the first onset of major depression, dysthymia, social anxiety disorder, specific phobia, generalized anxiety disorder, and panic disorder in a sample of 550 female adolescents who were assessed at baseline, and 528 and 513 of whom were assessed again at Waves 2 and 3, respectively, over the course of 18 months. Self-criticism and dependency predicted the first onset of a range of internalizing disorders. For depressive disorders, results primarily supported Personality × Stress models. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Adolescent Psychopathology; *Onset (Disorders); *Self-Criticism; *Adolescent Characteristics; Anxiety; Dependency (Personality); Internalization; Major Depression; Personality; Stress
- PsycINFO Classification:
- Psychological Disorders (3210)
- Population:
- Human
Female - Location:
- US
- Age Group:
- Adolescence (13-17 yrs)
- Tests & Measures:
- Big Five Inventory
Kiddie Schedule for the Affective Disorder--Past and Lifetime Version
Stressful Life Events Schedule--Adolescent Version
Family History Screen
Interpersonal Dependency Inventory DOI: 10.1037/t20072-000
Stressful Life Events Schedule DOI: 10.1037/t39244-000
Depressive Experiences Questionnaire DOI: 10.1037/t02165-000
Revised Depressive Experiences Questionnaire DOI: 10.1037/t06703-000 - Grant Sponsorship:
- Sponsor: National Institute of Mental Health, US
Grant Number: R01 MH093479
Recipients: Kotov, Roman
Sponsor: National Institute of Mental Health, US
Grant Number: RO1 MH45757
Recipients: Klein, Daniel N.
Sponsor: Social Sciences and Humanities Research Council of Canada, Canada
Other Details: postdoctoral fellowship
Recipients: Kopala-Sibley, Daniel C. - Methodology:
- Empirical Study; Followup Study; Longitudinal Study; Interview
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Jun 20, 2017; Revised: Jun 17, 2017; Aug 26, 2016
- Release Date:
- 20171120
- Copyright:
- American Psychological Association. 2017
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/abn0000297
- Accession Number:
- 2017-51268-002
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2017-51268-002&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2017-51268-002&site=ehost-live">Self-criticism and dependency in female adolescents: Prediction of first onsets and disentangling the relationships between personality, stressful life events, and internalizing psychopathology.</A>
- Database:
- PsycINFO
Self-Criticism and Dependency in Female Adolescents: Prediction of First Onsets and Disentangling the Relationships Between Personality, Stressful Life Events, and Internalizing Psychopathology
By: Daniel C. Kopala-Sibley
Department of Psychology, Stony Brook University;
Daniel N. Klein
Department of Psychology and Department of Psychiatry, Stony Brook University
Greg Perlman
Department of Psychiatry, Stony Brook University
Roman Kotov
Department of Psychiatry, Stony Brook University
Acknowledgement: This research was supported by National Institute of Mental Health (NIMH) Grant R01 MH093479 (Kotov) and NIMH Grant RO1 MH45757 (Klein), as well as postdoctoral fellowship from the Social Sciences and Humanities Research Council of Canada (Kopala-Sibley).
Self-criticism and dependency are associated with a wide variety of indicators of psychosocial functioning including psychopathology (Blatt, 2004; Blatt & Zuroff, 1992), social (Fichman, Koestner, & Zuroff, 1994; Kopala-Sibley, Rappaport, Sutton, Moskowitz, & Zuroff, 2013) and romantic relationship functioning (Lassri & Shahar, 2012), and academic achievement (Zuroff, 1994). Whereas there is substantial evidence that other personality traits are associated with the development of symptoms of depression and anxiety, and with onsets of these disorders (Clark, 2005; Klein, Kotov, & Bufferd, 2011; Krueger & Tackett, 2003), self-criticism and dependency have yet to be studied as predictors of first-onset psychological disorders. Thus, the first aim of this study was to test whether self-criticism and dependency predict the first lifetime onsets of a range of anxiety and depressive disorders in a large sample of female adolescents. In this paper, first-onset refers to the first time a participant met DSM–IV-TR criteria for that disorder, rather than the first time they developed any symptoms of the disorder.
If personality traits predict first onsets, the next step involves understanding how and under what conditions they do so. A number of conceptual models have been proposed to understand personality-psychopathology associations, including the precursor, diathesis-stress, and consequences models (e.g., Klein et al., 2011). Studies testing these models statistically typically examine only one model at a time, which likely reflects the difficulty and burden associated with collecting all the data needed to examine multiple analytic models (i.e., large sample, multiwave, cross-modal). Yet authors often discuss these conceptual models in competing terms, as if one or more were likely to be correct or receive more support than others. Alternatively, several analytic models may be supported by the data, although some may provide more unique explanatory power than others. The second aim of this study is to simultaneously examine these various perspectives in one analytic model predicting later depressive disorders.
Self-Criticism, Dependency and Internalizing DisordersBlatt and colleagues (e.g., Blatt & Luyten, 2009; Blatt & Zuroff, 1992; Kopala-Sibley & Zuroff, 2014) articulated a two-polarities theoretical model, according to which self-definition, or one’s sense of self, and relatedness, or one’s sense of relationships with close others, represent life span developmental tasks that are fundamental to both healthy functioning and the development of psychopathology. Delays or deficits in the development of a healthy sense of relatedness, due to negative developmental experiences, may lead to high levels of a personality style labeled dependency. This trait is characterized by fears of abandonment as well as insecurity regarding close others and a sense of self-worth that is contingent upon the care and support of others. Notably, other theorists have developed similar conceptualizations of interpersonal dependency (Hirschfeld et al., 1977; Bornstein, 1994, 1997), which they characterized as thoughts, feelings, and behaviors involving excessive emotional reliance on others, a strong need for contact with and attachment to others, high needs for approval, and excessive fears of abandonment.
In contrast, delays or deficits in the development of self-definition, again due to adverse developmental experiences, may lead to high levels of a personality style labeled self-criticism. Highly self-critical individuals are permeated with feelings of low self-worth and guilt, and have excessive needs to ascertain, confirm, and preserve status and value in the eyes of important others (see Blatt, D’Afflitti, & Quinlan, 1976; Blatt & Luyten, 2009; Kopala-Sibley & Zuroff, 2014 for reviews).
Although originally formulated as risk factors specific to depression (see Zuroff, Mongrain, & Santor, 2004, for an overview), research has shown that self-criticism and dependency, or closely related variables such as perfectionism, autonomy, and sociotropy, are associated with a range of disorders, including perimenopausal depression (Mauas, Kopala-Sibley, & Zuroff, 2014), social anxiety disorder (Kopala-Sibley, Zuroff, Russell, & Moskowitz, 2014), and generalized anxiety disorder and panic disorder (Antony, Purdon, Huta, & Swinson, 1998). As such, these traits may represent transdiagnostic risk factors for psychopathology. Whereas the bulk of this work has been limited to adults, self-criticism and dependency are also longitudinally associated with higher levels of depressive and anxiety symptoms, and heighten the effect of negative events on depressive symptoms, in children and early adolescents (e.g., Abela, Webb, Wagner, Ho, & Adams, 2006; Kopala-Sibley, Zuroff, Hankin, & Abela, 2015).
Whereas some research in adults has found that dependency predicts the occurrence of depressive episodes in adults (Dunkley, Zuroff, & Blankstein, 2006), only one study has examined whether dependency predicts the first onset of depressive disorders. Hirschfeld et al. (1989) found that interpersonal dependency predicted the onset of major depression in adults aged 31–41 years. This question has not been examined in adolescents, and no studies have examined whether dependency predicts first onsets of anxiety disorders. It is also unknown whether self-criticism predicts the first onset of any internalizing disorders in youth or adults.
Distinguishing Self-Definition and Dependency From NeuroticismConcerns have been raised over whether self-definition and relatedness are distinct from broader personality traits, in particular Neuroticism (e.g., Coyne & Whiffen, 1995, although see Zuroff et al., 2004 for a response). Self-criticism is moderately related to Neuroticism (Pearson rs of approximately .40–.60; Mongrain, 1993; Zuroff, 1994); the association between dependency and Neuroticism is weaker (Bagby & Rector, 1998; Clara, Cox, & Enns, 2003). Despite the moderate overlap of self-criticism with Neuroticism, self-criticism has shown incremental utility in predicting outcomes. For instance, adjusting for Neuroticism, self-criticism uniquely longitudinally predicts depressive symptoms, the occurrence of major depression, and global psychosocial functioning (Clara et al., 2003; Dunkley et al., 2006; Mongrain & Leather, 2006; see Smith et al., 2016 for a meta-analysis), although these studies did not examine dependency. Self-criticism and dependency also predict social anxiety disorder diagnoses (Cox et al., 2000; Kopala-Sibley et al., 2014) and negative affect in borderline personality disorder patients (Kopala-Sibley, Zuroff, Russell, Moskowitz, & Paris, 2012) over and above the effects of Neuroticism. However, no research has examined the incremental utility of dependency and self-criticism in predicting onsets, much less first onsets, of a variety of internalizing disorders, over and above Neuroticism.
Conceptual Models of Personality-Stress-Psychopathology RelationshipsThe interrelationship of personality, stressful life events, and psychopathology can be characterized by several plausible conceptual models (Clark, 2005; Klein et al., 2011; Krueger & Tackett, 2003). The precursor model posits that personality traits are antecedents and predictors of psychopathology. To the extent that other factors, such as life events (i.e., the stress reactivity model), also influence psychopathology, their effects are independent of personality, resulting in an additive model of personality and stress on psychopathology (Kushner, 2015).
Another influential set of theoretical models posit that traits moderate the effect of stressful life events on psychopathology (see Kushner, 2015 for a recent review). The most common conceptual model within this perspective is the diathesis-stress model (e.g., Blatt & Zuroff, 1992), which assumes psychopathology is produced by high levels of both the predisposing trait and life stressors. However, an alternative variant of Trait × Stress moderation models is the social push (Raine, 2002) or dual vulnerability (Morris, Ciesla, & Garber, 2008) model, which posits that either high levels of the trait or high levels of stress can produce psychopathology, but the absence of psychopathology requires low levels of both the trait and life stress.
Stressful life events or other environmental factors may also influence personality development (Klein et al., 2011; Kopala-Sibley & Zuroff, 2014; Ormel, Oldehinkel, & Brilman, 2001). Indeed, multiple studies indicate that adverse developmental and environmental experiences contribute to personality change (e.g., Roberts, Caspi, & Moffitt, 2003; Scollon & Diener, 2006), including change in self-criticism and dependency (Kopala-Sibley & Zuroff, 2014).
Finally, the consequences theoretical model posits that psychopathology may have persisting effects on personality traits (Klein et al., 2011). Results testing the consequences model of personality and depression have been inconsistent, with some evidence indicating that personality traits, including Neuroticism and dependency, are increased following a depressive episode (e.g., Fanous, Neale, Aggen, & Kendler, 2007; Rohde, Lewinsohn, & Seeley, 1990, 1994), whereas others have not found such an effect (e.g., Ormel, Oldehinkel, & Vollebergh, 2004; Shea et al., 1996). We are unaware of any research which has measured self-criticism and episodes of psychopathology repeatedly over time in order to test a consequences model.
Previous research has generally examined these conceptual models separately from one another. In order to make further progress in understanding the roles of personality and life stress in the etiology of psychopathology it is important to examine multiple conceptual models within a single analytic framework. Testing these conceptual models in separate statistical models, and usually in separate samples, cannot determine the contribution of each statistical path to the etiology of psychopathology over and above the effects of other paths.
To our knowledge, only two studies in any population have simultaneously statistically tested multiple conceptual models of personality and psychopathology in a single analytic framework, neither of which included life stressors (De Bolle, Beyers, De Clercq, & De Fruyt, 2012, De Clercq, De Caluwé, & Verbeke, 2016). De Bolle and colleagues assessed a large sample of children and young adolescents three times over the course of two years and found both correlated changes and reciprocal associations of normal range (De Bolle et al., 2012) and pathological (De Bolle et al., 2016) personality traits with internalizing and externalizing symptoms. The present study extends De Bolle et al. (2012, 2016) by including stressful life events and testing a broader range of conceptual models. As such, it represents a potentially important step forward in providing a more comprehensive understanding of the associations between personality and psychopathology.
Overview and HypothesesThe current study seeks to address several gaps in the existing literature. First, in a large sample of female adolescents who were assessed three times over an 18-month period, the current study examined whether the traits of self-criticism and dependency predict the first onset of a range of internalizing disorders. In addition, their predictive power over and above Neuroticism was examined. In all logistic regression analyses, baseline levels of symptoms of the predicted disorder was included as a covariate to rule out the possibility that any effects are due to prodromal cases in which the episode had started but not yet reached diagnostic threshold. This rendered the logistic models highly conservative given that prior subthreshold symptoms are among the most robust predictors of subsequent full threshold disorders (Burcusa & Iacono, 2007; Klein et al., 2013; Klein, Shankman, Lewinsohn, & Seeley, 2009).
Second, by measuring both personality traits and depressive disorders on three occasions, precursor (personality → depression), consequences (depression → personality), stress reactivity (stressful life events → depression), personality development (stressful life events → personality), and Trait × Stress moderation (Personality × Stressful Life Events → depression) conceptual models were examined. It should be noted the path models in this second set of analyses were limited to depression, as anxiety disorders were assessed at only two time-points, precluding testing most of these conceptual models. In addition, unlike in the logistic regression models described above where participants with a history of depression not otherwise (NOS) specified at baseline were excluded in order to predict first onsets of depressive disorders, these participants were not excluded when testing these path models. Rather, these analyses predicted later depression diagnoses, adjusting for the effects of prior depression diagnoses. It should also be noted that these analyses were not designed to test competing models; rather, they were intended to test multiple mutually compatible models under one analytic framework, an approach rarely taken in this literature.
In the logistic regression models, it was expected that both self-criticism and dependency would predict an increased likelihood of depressive (major depressive disorder [MDD], dysthymic disorder, and any depressive disorder) and anxiety disorders (generalized anxiety disorder, social anxiety disorder, panic disorder, specific phobia, and any anxiety disorder). In the path analyses, it was expected that several conceptual models of the relationship between personality and depression would be supported; however, there were no a priori predictions for which given that previous research has not tested them simultaneously.
Method Participants
At baseline, the sample consisted of 550 female adolescents aged 13.5–15.5 (Mage = 14.4, SD = 0.6) who participated as part of the Adolescent Development of Emotions and Personality Traits (ADEPT) project. ADEPT is a longitudinal study aiming to identify predictors of first onset depression and dysthymia. Thus, adolescent girls were excluded from enrollment if they met lifetime Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; DSM–IV–TR; American Psychiatric Association, 2000) criteria for major depressive, dysthymic or bipolar disorder at the initial assessment. Lifetime history of subthreshold depressive symptoms or DSM–IV-TR depression NOS were not exclusion criteria. The age range of 13.5–15.5 was selected because this is the period that immediately precedes the sharp increase in MDD incidence in girls (Hankin et al., 1998), thereby maximizing the yield of first onsets and minimizing the number of girls to be excluded. Adolescents were recruited through several methods, primarily by contacting families whose telephone numbers were purchased from a commercial list broker, but also word of mouth, school presentations, and advertisements. The racial or ethnic distribution was 80.5% Caucasian, 5.1% African American, 8.4% Latino, 2.5% Asian, 0.4% Native American, and 3.1% Other. Median household income was approximately $110,000 per year (adjusting cost of living, this is equivalent to a household income of $81,481 in the average location in the United States). Most (85.6%) participants lived in two-parent homes, and 51.4% of mothers and 46.9% of fathers had graduated from college. Adolescents who did not have a biological parent willing to participate in the study or had significant physical or cognitive disabilities that would prevent completion of all aspects of the study were excluded.
Of the 550 participants, all of whom had completed diagnostic interviews at baseline, 537, 500, and 511completed measures of self-criticism at Waves 1, 2, and 3, respectively. Similarly, 536, 492, and 505 youth completed measures of dependency at Waves 1, 2, and 3, respectively. At baseline, 548 completed a measure of Neuroticism. At Wave 2, 528 participants were interviewed regarding stressful life events and depressive diagnoses, and 513 completed diagnostic measures at Wave 3. In total, 104 participants had missing data on one or more variables at baseline as well as Wave 2 or 3 personality or diagnostic information, or stress at Wave 2. These 104 did not differ significantly from the 446 with complete data on all measures in terms of any demographic, personality, life events, or diagnostic variables in this paper (all ps > .05). This suggests that participants with complete data were representative of the original cohort. For path models, full information maximum likelihood procedures in Mplus v7.3 (Muthén & Muthén, 1998–2012) were used to estimate the means and intercepts to account for missing observations (Schafer & Graham, 2002).
Logistic regressions and polychoric correlations were computed for each specific anxiety disorder after removing participants who had that diagnosis at baseline, thereby allowing prediction of the first onset of each anxiety disorder. For logistic models predicting specific anxiety disorders, participants with a baseline (NOS) diagnosis for that specific disorder were also excluded. For logistic models predicting any anxiety disorder (which included first onsets of anxiety NOS), individuals with any anxiety NOS diagnosis at baseline were excluded. Participants who were diagnosed with depression NOS at baseline were removed in analyses predicting first onset of MDD, dysthymia, and any depressive disorder (the last of which included first onsets of depression NOS). NOS cases were handled differently in logistic models predicting depression than those predicting anxiety disorder because there are no MDD NOS or dysthymia NOS categories. In contrast, path models were computed including all participants. Thus, the logistic regression models predict the first onset of disorders, and the path models predict later diagnostic status after adjusting for the effects of diagnostic status at prior time points.
Procedure
The adolescents were assessed at three waves, each 9 months apart. At all three waves, participants completed a revised version (Bagby, Parker, Joffe, & Buis, 1994) of the Self-Criticism subscale of the Depressive Experiences Questionnaire (DEQ; Blatt et al., 1976), as well as the Emotional Dependency subscale of the Interpersonal Dependency Inventory (IDI; Hirschfeld et al., 1977). Participants completed the Neuroticism subscale of the Big Five Inventory (BFI) at baseline (John, Naumann, & Soto, 2008). At Wave 2, teens were interviewed with the Stressful Life Events Schedule (SLES; Williamson et al., 2003), from which total life events were scored. At Waves 1 and 3, participants were administered the Kiddie Schedule for the Affective Disorders Past and Lifetime version (K-SADS-PL; Kaufman et al., 1997). At Wave 2, the adolescents completed the depressive disorders section of the K-SADS-PL. All procedures were approved by the Institutional Review Board at Stony Brook University.
Materials
Self-criticism
Self-criticism was assessed with Bagby and colleagues’ (1994) 10-item revision of the Self-Criticism subscale of the DEQ (Blatt et al., 1976). An example of a self-criticism item is “There is a considerable difference between how I am now and how I would like to be.” Participants are asked to judge the extent to which they agree or disagree with each statement on a 5-point scale (1 = Disagree strongly, 5 = Agree strongly). The Self-Criticism subscale of the DEQ has shown acceptable internal consistency and excellent test–retest reliability, and discriminated between depressed outpatients and healthy controls (Bagby et al., 1994). In the current study, Cronbach’s alpha for the Self-Criticism scale at Waves 1, 2, and 3, were .86, .88, and .88, respectively. Stability coefficients for self-criticism from Waves 1 to 2, 2 to 3, and 1 to 3, were .69, .70, and .58, respectively.
Dependency
Dependency was assessed by the IDI (Hirschfeld et al., 1977), a widely used measure of trait dependency. Using principal components analysis, Hirschfeld and colleagues (1977) found that the IDI items loaded onto three subscales: Emotional Reliance on Another Person (ER), Lack of Social Self-Confidence, and Assertion of Autonomy. The current paper focused on the emotional reliance subscale, which is comprised of six items (e.g., “Disapproval by someone I care about is very painful for me”). The IDI subscales demonstrated acceptable reliability (Hirschfeld et al., 1977), and retest reliability over intervals ranging from 16 to 84 weeks (Bornstein, 1994, 1997). The IDI distinguishes between depressed and healthy individuals (Hirschfeld et al., 1977), and is associated with other self-report and behavioral measures of dependency (Bornstein, 1994; Hirschfeld, Klerman, Clayton, & Keller, 1983). In addition, emotional reliance has predicted the onset of major depression in adults (Hirschfeld et al., 1989), and adolescents’ scores on the ER subscale predicted subsequent MDD episodes in young adulthood (Lewinsohn, Rohde, Seeley, Klein, & Gotlib, 2000). In the present study, the ER subscale had alphas of .86, .88, and .87 at Waves 1, 2, and 3, respectively. Stability coefficients from Waves 1 to 2, 2 to 3, and 1 to 3, were .62, .57, and .49, respectively.
Neuroticism
Neuroticism was assessed at baseline with the self-report BFI (John et al., 2008; John, Donahue, & Kentle, 1991). The BFI asks participants to rate extent to which a series of statements describes them on a scale of 1 (Disagree strongly) to 5 (Agree strongly). Example items are “is emotionally stable, not easily upset,” (reversed) and “can be moody.” Neuroticism scores on the BFI have been associated with depressive and anxiety symptoms in the general population as well as internalizing diagnoses in psychiatric populations (Gosling, Rentfrow, & Swann, 2003; Kotov, Gamez, Schmidt, & Watson, 2010; Rammstedt & John, 2007). In the current study, the Neuroticism subscale had an alpha of .83.
Life events
Stressful life events were assessed with the SLES, adolescent version (Williamson et al., 2003), a semistructured interview that focuses on life events occurring during the previous 9 months. It covers events from a range of domains of relevance to adolescents, including parents, peers, romantic partners, siblings, and academic performance, as well as other domains, such as health and family finances. Interviews were conducted with the adolescent by undergraduate research assistants and postbachelors and masters-level staff who were trained and supervised by a team of clinical psychologists (GP, DK, RK) and experienced staff members. Training included didactics, supervised role playing, and observing several interviews by trained interviewers. Following established SLES procedures (Williamson et al., 2003), raters met as a group to establish consensus ratings of objective threat for each event. Objective threat was coded on a scale from 1 (little or no effect) to 4 (great effect) using the descriptors provided in the manual. The stress score was the sum of objective threat ratings for all events. In prior research, the SLES has shown good interrater reliability for coding objective threat, and discriminates between children with and without psychopathology (Williamson et al., 2003).
Internalizing disorders
Psychopathology was assessed with the K-SADS-PL (Kaufman et al., 1997), a widely used semistructured diagnostic interview designed to assess current and past episodes of psychopathology in children and adolescents according to DSM–IV-TR criteria. K-SADS interviews were conducted with the adolescent by postbachelors or masters-level staff who were trained and supervised by a team of clinical psychologists (GP, DK, RK). Training included didactics, supervised role playing, and observing several interviews by trained interviewers. Quality control was maintained through weekly supervision meetings for discussion and feedback and reliability of video-recorded interviews. Interviews with parents about cardinal symptoms of depression and anxiety in the child using the Family History Screen (Weissman et al., 2000) were also conducted. If parents described symptoms that their child did not report, interviewers clarified the discrepancies with the teens and revised K-SADS ratings. Depressive disorders were assessed at Waves 1, 2, and 3, whereas anxiety disorders were assessed at Waves 1 and 3. Analyses focus on major depression, dysthymia, any depressive disorder (including NOS), social anxiety disorder, specific phobia, generalized anxiety disorder, panic disorder, and any anxiety disorder (including NOS). An independent rater derived diagnoses from videotapes of 40 interviews to establish interrater reliability. Kappas for specific diagnoses ranged from .62 (depression NOS) to .91 (generalized anxiety disorder), with a median kappa of .79. The reliability of diagnoses of any depressive and any anxiety disorder were Kappas = .81 and .75, respectively.
The prevalence of each diagnosis at baseline and over the follow-up period, as well as the number of first-onset cases, is listed in Table 1. Subthreshold symptoms refer to significant symptoms of a specific disorder that fell short of meeting full criteria for that disorder and were not impairing enough to warrant an NOS diagnosis. Due to the diversity of clinical syndromes subsumed by anxiety disorders, when anxiety NOS diagnoses were assigned, interviewers rated which specific anxiety disorder they corresponded most closely to.
Prevalences at Baseline and Follow Up and Number of First Onsets of Each Disorder
Data Analyses
Analyses consisted of two parts. First, via a series of logistic regression models, diagnostic status for each disorder at Wave 3 was regressed on either self-criticism or dependency. In order to examine first onsets, within each logistic regression model any participants who had a history of that specific diagnosis at baseline were dropped. Thus, the logistic regressions compared the first onset group to the unaffected group, and the effective sample size differed for each disorder. In logistic regression models predicting MDD, dysthymia, and any depressive disorder (including depression NOS), participants with a prior diagnosis of depression NOS were excluded. In logistic analyses predicting each specific anxiety disorder, participants with a prior full or NOS diagnosis for the disorder examined in that model were excluded. In logistic analyses predicting any anxiety disorder (including cases with anxiety NOS), participants with any prior anxiety disorder, including any anxiety NOS diagnosis, were removed from the analysis. As anxiety NOS and depression NOS are not specific clinical syndromes, they were not included as outcomes on their own.
In all logistic regression analyses, baseline subthreshold status of the predicted disorder was included as a covariate. As noted earlier, this is a highly conservative approach, given that prior subthreshold disorders are a robust predictor of subsequent full threshold disorders (Burcusa & Iacono, 2007; Klein et al., 2009, 2013). Baseline personality traits were standardized (M = 0, SD = 1). Odds ratios with confidence intervals, as well as polychoric correlations are reported as measures of effect size. Of note, whereas predictors of first onsets of each disorder were examined separately, some participants experienced first onsets of multiple disorders. Specifically, 36 (7.0%) participants experienced the first onset of both a depressive and anxiety disorder, and 87 (16.4%) experienced the first onset of more than one anxiety disorder during the follow up.
Logistic regressions were repeated after covarying baseline Neuroticism in order to examine whether the effects of predictors showed incremental predictive utility over and above this higher order personality trait. Logistic regressions were again repeated after including both self-criticism and dependency in the same models in order to examine their incremental predictive utility relative to one another as well as any specificity in their effects on the first onsets of internalizing disorders.
The second set of analyses consisted of cross-lagged panel analyses in Mplus 7.3 (Muthén & Muthén, 1998–2012; Figure 1). Path models only examined depressive disorders, as these were assessed at all three time points, whereas anxiety disorders were only assessed at baseline and Wave 3, precluding adequate testing of many of the models described above. Rather than examining first onsets of depression, the path analyses predicted later depressive disorder diagnosis (including NOS) after covarying the effects of prior depressive disorder diagnosis. Path models used the full sample, and baseline depression NOS cases were treated as covariates. This was done so that path models could examine personality-depression relationships, such as consequences effects, yet still ensure that effects of personality, stress, and their interaction on later depressive disorders were not due to baseline depressive disorders. Path models predicting depressive disorders at Waves 2 and 3 examined the consequences (Path A), precursor (Path B), personality development (Path C), Personality × Stress (Path D), and stress-reactivity (Path E) conceptual models (see Figure 1). A path from Wave 2 depressive diagnoses to Wave 3 personality was included to test the consequences models, but not from Wave 1 depression, given that the only possible depression diagnosis at Wave 1 was depression NOS, which does not provide an adequate test of this conceptual model. Effects of baseline personality or depression NOS on Wave 2 stress (i.e., stress generation) were not included because stress was not measured at baseline, and we therefore could not adjust for its baseline levels. Wave 2 stress comprises events that occurred during the interval between Waves 1 and 2. Wave 1 personality, therefore, reflects personality prior to the stressors, whereas Wave 2 personality assesses traits following the stressors. Thus, our models examine the interaction of Wave 1 personality with stress occurring subsequent to the measurement of the personality trait. Moderation was examined via the Johnson-Neyman (JN) index, also known as a regions of significance test (Johnson & Neyman, 1936; Bauer & Curran, 2005). We were primarily interested in the effects of stress on depressive diagnoses at different levels of personality, but also examined the effects of personality at different levels of stress.
Figure 1. Conceptual overview of the analytic models linking personality, stress, and depressive disorders. (Path A) consequences; (Path B) precursor; (Path C) personality development; (Path D) Personality × Stress; (Path E) stress reactivity.
Results Descriptive Statistics and Bivariate Correlations
The prevalence of each disorder and the number of first onsets are shown in Table 1. Specific phobia had the largest, and dysthymia the fewest number of first onsets. Descriptive statistics and bivariate correlations between personality at Waves 1, 2, and 3 and stress at Wave 2 are shown in Table 2. Baseline self-criticism, dependency, and Neuroticism were positively associated with greater levels of stress at Wave 2, whereas Wave 2 stress was positively associated with self-criticism and dependency at Waves 2 and 3. Baseline Neuroticism was positively correlated with dependency and self-criticism at all three waves.
Descriptive Statistics and Bivariate Correlations
Predicting First Lifetime Onsets of Disorders
Results of logistic regression analyses regressing the first onsets of depressive and anxiety disorders on self-criticism and dependency appear in Table 3. Self-criticism significantly predicted the first onset of all disorders except panic and MDD. Dependency also significantly predicted the first onset of all disorders except major depression, and, at a trend level, social anxiety disorder.
Results of Baseline Personality Traits Predicting First Onsets of Disorders
Adjusting for Neuroticism (Table 4), self-criticism predicted the first onset of dysthymia, whereas Neuroticism was nonsignificant. Neuroticism predicted the first onsets of any anxiety disorder and any depressive disorder, whereas in both cases self-criticism exhibited nonsignificant trend-level effects. Neither trait uniquely predicted the first onset of social anxiety disorder, generalized anxiety disorder, panic disorder, specific phobia, or major depression.
Results of Baseline Personality Traits and Neuroticism Predicting First Onsets of Disorders
Adjusting for Neuroticism, dependency significantly predicted the first onset of generalized anxiety disorder, specific phobia, and any anxiety disorder. Neuroticism was significantly related to any anxiety disorder, major depression, and any depressive disorder, adjusting for dependency. Neither trait uniquely predicted social anxiety disorder, panic disorder, or dysthymia.
Adjusting for the effects of both dependency and self-criticism (Table 5), dependency, but not self-criticism, uniquely predicted the first onset of generalized anxiety disorder, specific phobia, and any anxiety disorder. Self-criticism, but not dependency, uniquely predicted the first onset of dysthymia and any depressive disorder.
Results of Dependency Versus Self-Criticism Predicting First Onsets of Disorders
Path Models
Path models simultaneously tested the various conceptual models discussed above and focused on the presence of any depressive disorder at Waves 2 and 3. Dependency and self-criticism were evaluated in separate path models as incorporating both traits at each wave as well their interactions with stress predicting psychopathology while adjusting for baseline Neuroticism would require a much larger sample size for adequate power. Both models adjusted for the effects of baseline Neuroticism on Wave 2 and 3 depressive disorders (Figure 2). Wave 1 and 2 variables were covaried within time points, but Wave 3 variables were not covaried as Mplus cannot estimate covariances between categorical and continuous dependent variables in the presence of missing data. In models with both continuous and categorical endogenous variables and missing data, Mplus employs a Monte Carlo integration algorithm which precludes use of theta parameterization. The default delta parameterization was therefore used.
Figure 2. Interrelationships between personality, stress, and depression. Transparent gray lines indicate nonsignificant paths. Covariances between endogenous variables not included for visual clarity, although Time 3 variables were not covaried. T1 = Time 1; T2 = Time 2; T3 = Time 3; OR = odds ratio. * p < .05. ** p < .01.
Self-criticism and depressive disorders
Wave 1 self-criticism predicted an increased likelihood of a depressive disorder at Wave 2 and Wave 2 self-criticism predicted an increased likelihood of a depressive disorder at Wave 3 (Figure 2A, top panel). Wave 2 life events predicted increased self-criticism at Wave 3 and a greater likelihood of a depressive disorder at Wave 3. There was no significant effect of depressive disorders at Wave 2 on Wave 3 self-criticism. Finally, there was a significant interaction between baseline self-criticism and Wave 2 life events predicting Wave 3 depressive disorders. Life events predicted an increased likelihood of a depressive disorder only when self-criticism was less than 0.3 standard deviations above the mean (Figure 3A, top panel). Results showed that greater life stress predicted an increased likelihood of a depressive disorder at the 10th (β = 1.20, p < .001), 25th (β = 1.01, p < .001), and 50th (β = .70, p < .001) percentiles of self-criticism, but not at the 75th (p = .24) or 90th (p = .55) percentiles. Examining stress as the moderator, greater levels of self-criticism predicted an increased likelihood of a depressive disorder only when stress was less than 0.35 standard deviations above the mean. Greater self-criticism predicted an increased likelihood of a depressive disorder at the 10th (β = .96, p = .003), 25th (β = .82, p < .005), and 50th (β = .53, p = .03) percentiles of life stress, but not at the 75th (p = .87) or 90th (p = .27) percentiles.
Figure 3. Effect of stress on the probability of a depressive disorder at varying levels of baseline self-criticism (Panel A) or dependency (Panel B). For both panels, only slopes at 10th, 25th, and 50th percentile levels are significant. Dep = dependency; SC = self-criticism.
Dependency and depressive disorders
Baseline dependency did not predict Wave 2 depressive diagnoses, although it did predict Wave 3 depressive diagnoses (Figure 2B, bottom panel). Wave 2 depressive disorders did not predict changes in Wave 3 dependency. Stressful life events predicted a greater likelihood of a depressive disorder at Wave 3, but did not predict dependency at Wave 3. Finally, there was a significant interaction between baseline dependency and Wave 2 life events predicting Wave 3 depressive disorders. The JN analysis showed that more life events predicted an increased likelihood of a depressive disorder only when dependency was less than 0.6 standard deviations above the group mean. Specifically, life events (Figure 3B, bottom panel) predicted an increased likelihood of a depressive disorder at the10th (β = .98, p < .001,) 25th (β = .81, p < .001); and 50th (β = .52, p < .001); but not at the 75th (p = .34); or 90th percentile (p = .71). Examining stress as the moderator, greater levels of dependency predicted an increased likelihood of a depressive disorder only when stress was less than 0.6 standard deviations above the mean. Greater dependency predicted an increased likelihood of a depressive disorder at the 10th (β = .69, p < .03) and 25th (β = .59, p = .03) percentiles, and showed a nonsignificant trend at the 50th (β = .38, p = .08) percentiles of life stress, but was not significant at the 75th (p = .89) or 90th (p = .35) percentiles.
DiscussionA series of logistic regression analyses revealed that the personality traits of self-criticism and dependency predict the first lifetime onset of a range of depressive and anxiety disorders over a period of 18 months in a sample of female adolescents. Moreover, a number of the effects, particularly for dependency, remained significant after adjusting for Neuroticism, which, in many cases, was not significant over and above self-criticism or dependency. Results suggest that self-criticism and dependency predict risk for the first onset of internalizing disorders in early female adolescents, thereby informing clinicians’ ability to identify and potentially intervene with young female adolescents prior to such onsets.
Second, path analyses that predicted depressive disorders at Wave 2 or 3 adjusting for prior depression tested a series of conceptual models that could account for these predictive effects. For both self-criticism and dependency, precursor, stress reactivity, and Personality × Stress paths predicted depressive diagnoses. The personality development model was supported for self-criticism, but not dependency, and there was no support for the consequences model in for either trait. Taken together, given that stress-reactivity and precursor effects were qualified by their interaction, results primarily support Personality × Stress models, in the form of dual vulnerability, as well as personality development in terms of the effects of stress on self-criticism.
Predicting First Onsets of Disorders
Rates of depressive and anxiety diagnoses were similar to those found in epidemiological surveys of adolescents (Merikangas et al., 2010), and the total cumulative rates of anxiety and depressive disorders by Wave 3 was similar to longitudinal community surveys (Moffitt et al., 2010), suggesting that the prevalence of internalizing disorders in the current sample is broadly comparable to other community samples. Results from logistic regression analyses showed that both self-criticism and dependency confer an increased risk for the first lifetime onset of most internalizing disorders, although neither significantly predicted major depression or panic disorder on its own. The stronger effects on dysthymia than major depression are consistent with evidence (Klein & Black, 2017; Kotov et al., 2010) that chronic depression is more strongly related to trait vulnerabilities, whereas acute major depression may be more strongly related to life stressors.
We then conducted analyses adjusted for Neuroticism, given concerns that self-criticism or dependency are so highly saturated with this broad trait that they may not have any unique effects (Coyne & Whiffen, 1995). Consistent with previous evidence of the incremental utility of these two personality traits (Smith et al., 2016; Zuroff et al., 2004), after adjusting for Neuroticism, self-criticism continued to predict the first onset of dysthymia and showed a nonsignificant trend toward predicting the first onset of any anxiety disorder and any depressive disorder, whereas dependency continued to predict the first onset of generalized anxiety disorder, specific phobia, and any anxiety disorder. The inclusion of Neuroticism in the logistic regression models eliminated the significant effects of self-criticism on first onsets of social anxiety disorder, generalized anxiety disorder, and specific phobia, and the significant effects of dependency on onsets of panic disorder, dysthymia, and any depressive disorder. Although Neuroticism independently predicted the onset of any anxiety and any depressive disorders, it did not uniquely predict the onset of any specific internalizing disorder other than major depression when self-criticism or dependency was included in the logistic regression model. Thus, despite its broader content, Neuroticism failed to account for additional variance in the onset of most specific internalizing disorders over and above the narrower traits of self-criticism and dependency. Although highly correlated predictors such as Neuroticism and self-criticism or dependency are subject to a degree of fungibility in multivariate analyses, findings suggest that dependency may be a unique predictor of the onset of anxiety disorders, over and above Neuroticism, whereas self-criticism may contribute unique variance in predicting the onset of dysthymia.
It is possible that much of the shared variance between Neuroticism, self-criticism, and dependency is due to each being characterized by emotional dysregulation and tendencies toward negative affect (see Zuroff, 1994; Zuroff et al., 2004). However, although Neuroticism is defined largely in terms of affective tendencies, self-criticism also measures one’s sense of self, personal standards, and expectations of others, and dependency assesses one’s sense and expectations of relationships with close others. As such, it is possible that these aspects of self-criticism and dependency influenced risk for internalizing psychopathology over and above Neuroticism in the current study.
When including self-criticism and dependency in the same models, the results appeared to bear out this relative specificity of dependency for risk of anxiety disorders onset and self-criticism for risk of depressive disorders onset. Indeed, dependency uniquely predicted the first onset of generalized anxiety disorder, specific phobia, and any anxiety disorder, whereas self-criticism uniquely predicted the first onset of dysthymia and any depressive disorder. However, given that there were broader transdiagnostic effects of each personality trait when considered individually and when not adjusting for Neuroticism, future research should further elucidate the shared and unique predictive utility of different personality traits for the first onset of internalizing disorders.
Elucidating the Personality-Life Stress-Psychopathology Relationship
The prospective links between traits, stressful life events, and depressive disorders were examined over 18 months. The conceptual models reflected in these paths are typically tested individually rather than within the same analytic framework, which can lead to a biased or incomplete understanding of dynamic associations among key variables. It is important to note, however, that the path models in this paper were not predicting first onsets of depressive disorders. Rather, they were predicting later diagnostic status after adjusting for the effects of prior diagnostic status.
There was consistent support for the precursor model of personality-depression relationships. That is, while controlling for Neuroticism and prior history of depression NOS at Wave 1, both self-criticism and dependency predicted subsequent depressive diagnoses. Consistent with a large body of literature showing an effect of stress on depression (e.g., Monroe, Slavich, & Georgiades, 2014), there was also support for stress-reactivity models. However, both of these effects were qualified by the interaction between stress and personality, such that stress only predicted depressive disorders at lower levels of self-criticism or dependency. Alternatively, these effects may be interpreted as there being a greater effect of personality on depressive diagnoses at lower levels of stress.
Consistent with a variety of prior developmental studies (see Kopala-Sibley & Zuroff, 2014; Neyer & Asendorpf, 2001; Ormel et al., 2001; Scollon & Diener, 2006), stress predicted change in personality traits, but only for self-criticism. This suggests that high levels of stressful life events in early adolescence may compound this personality-level risk factor for psychopathology. However, contrary to prior work (e.g., Kopala-Sibley et al., 2012, 2015; Kopala-Sibley, Zuroff, Hermanto, & Joyal-Desmarais, 2016; Soenens, Vansteenkiste, & Luyten, 2010), effects of stress on dependency were not found. The reasons for this are unclear. It is possible that only events pertaining to specific life domains may influence dependency, especially relationship-centered stressors (Kopala-Sibley, Zuroff, Leybman, & Hope, 2012; Kopala-Sibley, Zuroff, Hermanto, & Joyal-Desmarais, 2016; Soenens et al., 2010). Moreover, it is possible that stressors may be related to specific aspects of dependency, such as connectedness, which is a more adaptive form, versus neediness, which is more maladaptive (see Rude & Burnham, 1995).
In contrast to the findings for the other conceptual models, results did not support the consequences model, as depression did not predict subsequent self-criticism or dependency. These results are consistent with some research indicating that personality traits are not increased following a depressive episode (e.g., Ormel et al., 2004; Shea et al., 1996), although inconsistent with other work that has found such an effect (Fanous et al., 2007; Rohde et al., 1990, 1994). The reason for these contradictory findings is unclear.
Path models for both self-criticism and dependency revealed significant interactions between personality traits and stressful life events in predicting subsequent depressive disorders. Most studies of personality by stress interactions conceptualize them from a diathesis-stress perspective (e.g., Brown & Rosellini, 2011; Kendler, Kuhn, & Prescott, 2004; Kopala-Sibley, Kotov, et al., 2016). However, the present findings provide strong support for the dual-vulnerability or social push model instead (Kushner, 2015; Morris et al., 2008). That is, individuals with highly elevated levels of self-criticism or dependency showed an increased likelihood of a subsequent depressive disorder, regardless of the level of life stressors they experienced. This would be consistent with the precursor model, albeit only for the subset of adolescents with elevated trait vulnerabilities. In contrast, youth with lower levels of self-criticism or dependency exhibited higher rates of internalizing disorders only when subjected to a high level of stressful life events. Thus, stress reactivity is an appropriate way to understand the relationship between life events and internalizing psychopathology for those lower in self-criticism or dependency.
Finally, results should be interpreted in a developmental context, as participants in this study underwent substantial changes in socioemotional and personality development. Consistent with other studies examining self-criticism and dependency in adolescence (Kopala-Sibley et al., 2015; Thompson, Zuroff, & Hindi, 2012), as well as broader personality traits such as the Big Five (Roberts & DellVecchio, 2000), self-criticism and dependency showed only moderate stabilities over time, suggesting that these traits are more fluid in adolescence relative to adulthood. As noted by Blatt (e.g., Blatt & Luyten, 2009), early adolescence is a key period for the development of self-definition and relatedness. Whereas all adolescents deal with individuation and new forms of relatedness, females may be in a particularly unique developmental context as friendships and romantic relationships take on especially important roles (Blatt & Luyten, 2009). More highly dependent or self-critical female teens appear to be at risk for depression regardless of these stressors, which appear to play a particularly important role in depression for less dependent or self-critical female adolescents. For early adolescents with lower levels of these personality traits, who are also navigating stressful life events that are new or assume greater importance than before, life stressors appear to increase risk for depressive disorders even in the absence of personality-level vulnerabilities.
Clinical ImplicationsResults suggest that practitioners should be cognizant of levels of self-criticism or dependency in female adolescents as these appear to increase risk for internalizing psychopathology, although there are multiple other risk factors to consider as well. Youth may benefit from interventions designed to directly reduce levels of dependency or self-criticism, such as self-compassion-based psychotherapy (Gilbert, 2009; Kelly, Zuroff, & Shapira, 2009). On the contrary, for those lower in dependency or self-criticism, interventions may seek to bolster individuals’ capacity to cope with stress (e.g., social skills training, interpersonal psychotherapy, cognitive-behavioral psychotherapy). Given interactions between these traits and life events, distinct interventions may be beneficial for adolescents who have elevated compared with low levels of these traits but are experiencing high levels of life stress. Further follow-up waves are required to test these models pertaining to anxiety disorders; it is unclear if the same conclusions would apply to anxiety-related psychopathology.
Limitations and Future Directions
Although this study had some notable strengths, including a large sample assessed at three waves over 18 months, as well as the use of semistructured interviews to establish diagnoses and to assess stressful life events, several limitations should be acknowledged. First, female adolescents were enrolled in order to maximize first onsets of depressive disorders. Adolescence is the beginning of the peak risk period for the onset of depression, with rates increasing more rapidly among females than males (e.g., Hankin et al., 1998). However, the present results may not extend to males or to other age groups, such as children and adults. Relatedly, although the sample was representative of the socioeconomic makeup of the geographical region in which this study was conducted, it was somewhat greater in terms of education and income than the national average and had a larger proportion of Caucasians. It is therefore unclear whether results would generalize to other socioeconomic, ethnic, or racial groups.
Second, the number of first onsets of some disorders, such as dysthymia and panic, were small, reducing power to detect disorder-specific effects. Third, the current study examined total stressful life events, so it is unknown whether results would extend to specific types of events (e.g., dependent and independent, or interpersonal and achievement). Fourth, there was primarily a single informant for all measures, which may raise concerns about inflated correlations due to method variance. This may have also resulted in some missing information, especially regarding stress. However, it should be noted that parents were also interviewed about youth’s psychopathology, diminishing concerns about biases or errors in diagnoses.
Finally, to fully elucidate the nature of prospective associations between variables, it is necessary to include all measures at all time points in the analytic model (Maxwell & Cole, 2007). As anxiety diagnoses were assessed only at baseline and Wave 3, they were not examined in path models. In addition, in the depression path models, stress was not measured at baseline precluding examining of the stress-generation conceptual model (Hammen, 2006). Moreover, consequences effects from Wave 1 depressive status to Wave 2 personality were not examined because only depression NOS cases were included at baseline, thereby precluding a proper test of this path.
Conclusion
The personality traits of self-criticism and dependency predicted the first onsets of a range of internalizing disorders in a sample of young female adolescents, a group that is particularly vulnerable to internalizing psychopathology. In addition, path models testing a variety of relationships between traits, life events and depression consistently supported Personality × Stress models, in the form of dual vulnerability. Thus, for the subgroup of participants with elevated self-criticism or dependency, traits appeared to be a precursor of depression, whereas for the subgroup with lower levels of trait vulnerability, depression was explained by stress-reactivity. This suggests that researchers and clinicians should consider personality by stress interactions in understanding depressive disorders, and that these interactions may take alternative forms than the classic diathesis-stress formulation. More broadly, the present findings indicate that self-criticism and dependency predict risk for a range of internalizing disorders in female adolescents, and suggest that a variety of therapeutic strategies may be useful for these vulnerable youth.
Footnotes 1 However, if a path from personality or psychopathology to stress is included, baseline self-criticism, dependency, and depression NOS predict greater levels of stress, adjusting for Neuroticism. Other results are unchanged if paths from baseline personality and depression to stress are included.
2 Given concerns of normative developmental effects, analyses were repeated after including age at each wave in the model, as well as the interaction of age with each variable at each wave predicting outcomes at the subsequent wave. None of these effects were significant, and the pattern of results reported here was unchanged. Age was therefore dropped from our final models.
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Submitted: August 26, 2016 Revised: June 17, 2017 Accepted: June 20, 2017
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Source: Journal of Abnormal Psychology. Vol. 126. (8), Nov, 2017 pp. 1029-1043)
Accession Number: 2017-51268-002
Digital Object Identifier: 10.1037/abn0000297
Record: 46- Title:
- Self-harm and suicidal behavior in borderline personality disorder with and without bulimia nervosa.
- Authors:
- Reas, Deborah L.. Department of Eating Disorders, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway, deborah.lynn.reas@ous-hf.no
Pedersen, Geir. Department of Personality Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
Karterud, Sigmund. Institute for Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
Rø, Øyvind. Regional Department of Eating Disorders, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway - Address:
- Reas, Deborah L., Regional Department of Eating Disorders (RASP), Division of Mental Health and Addiction, Oslo University Hospital–Ullevål Hospital, P.O. Box 4956 Nydalen, N-0424, Oslo, Norway, deborah.lynn.reas@ous-hf.no
- Source:
- Journal of Consulting and Clinical Psychology, Vol 83(3), Jun, 2015. pp. 643-648.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 6
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- suicide, self-harm, Bulimia nervosa, borderline personality disorder
- Abstract (English):
- Objective: Few studies have investigated whether a diagnosis of Bulimia nervosa (BN) confers additional risk of life-threatening behaviors such as self-harm and suicidal behavior in borderline personality disorder (BPD). Method: Participants were 483 treatment-seeking women diagnosed with BPD according to the Structured Clinical Interview for DSM–IV Axis II Personality Disorders (SCID-II; First, Gibbon, Spitzer, Williams, & Benjamin, 1997; Diagnostic and Statistical Manual of Mental Disorders, 4th ed.; APA, 1994) and admitted to the Norwegian Network of Psychotherapeutic Day Hospitals between 1996 and 2009. Of these, 57 (11.8%) women met DSM–IV diagnostic criteria for BN according to the Mini-International Neuropsychiatric Interview (M.I.N.I.; Sheehan et al., 1998) and they were compared with women with BPD and other Axis I disorders. Results: We found that comorbid BN is uniquely and significantly associated with increased risk of suicidal behavior among women being treated for BPD. Findings underscore the importance of routinely screening for BN among women seeking treatment for BPD, as co-occurring bulimia appears to be a significant marker for immediate life-threatening behaviors in this already high-risk population, which is a significant public health issue. A significantly greater proportion of women with BPD-BN reported suicidal ideation at intake (past 7 days), engaged in self-harm behavior during treatment, and attempted suicide during treatment. All bivariate associations remained significant in the logistic regression models after controlling for mood, anxiety, and substance-related disorders. Conclusion: The presence of a concurrent diagnosis of BN among women with BPD is significantly and uniquely associated with recent suicidal ideation, and self-harm behavior and suicide attempts during treatment after controlling for major classes of mental disorders. Co-occurring BN appears to represent a significant marker for immediate life-threatening behaviors in women seeking treatment for BPD. Extra vigilance and careful monitoring of suicidal behavior during treatment is important for these individuals, and routine screening for BN is warranted. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Impact Statement:
- What is the public health significance of this article?—This study found that co-occurring bulimia nervosa is uniquely and significantly associated with increased risk of suicidal behavior among women being treated for borderline personality disorder. Findings underscore the importance of routinely screening for bulimia nervosa among women seeking treatment for borderline personality disorder, as co-occurring bulimia appears to be a significant marker for immediate life-threatening behaviors in this already high-risk population. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Attempted Suicide; *Borderline Personality Disorder; *Bulimia; *Self-Injurious Behavior; *Suicidal Ideation; Comorbidity; Risk Factors
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Aged; Borderline Personality Disorder; Bulimia Nervosa; Female; Humans; Middle Aged; Self-Injurious Behavior; Suicidal Ideation; Suicide, Attempted; Young Adult
- PsycINFO Classification:
- Psychological Disorders (3210)
- Population:
- Human
Female
Outpatient - Location:
- Norway
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs)
Aged (65 yrs & older) - Tests & Measures:
- Structured Clinical Interview for DSM-IV Axis II Personality Disorders
Mini International Neuropsychiatric Interview DOI: 10.1037/t18597-000 - Methodology:
- Empirical Study; Interview; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Dec 15, 2014; Accepted: Oct 31, 2014; Revised: Oct 29, 2014; First Submitted: Jun 6, 2014
- Release Date:
- 20141215
- Correction Date:
- 20160512
- Copyright:
- American Psychological Association. 2014
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/ccp0000014
- PMID:
- 25495360
- Accession Number:
- 2014-55558-001
- Number of Citations in Source:
- 34
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-55558-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-55558-001&site=ehost-live">Self-harm and suicidal behavior in borderline personality disorder with and without bulimia nervosa.</A>
- Database:
- PsycINFO
Self-Harm and Suicidal Behavior in Borderline Personality Disorder With and Without Bulimia Nervosa / BRIEF REPORT
By: Deborah L. Reas
Regional Department of Eating Disorders, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway;
Geir Pedersen
Department of Personality Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway, and Institute for Clinical Medicine, Faculty of Medicine, University of Oslo
Sigmund Karterud
Institute for Clinical Medicine, Faculty of Medicine, University of Oslo and Department of Research and Development, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
Øyvind Rø
Regional Department of Eating Disorders, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway and Institute for Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
Acknowledgement:
Borderline personality disorder (BPD) is characterized by a pervasive pattern of instability in interpersonal relationships, self-image, and affect (APA, 2013) and is marked by impulsivity and recurrent suicidal and self-mutilating behavior. Suicidal behavior and self-harm also occur among patients with Bulimia nervosa (BN; Corcos et al., 2002; Crow et al., 2009; Franko & Keel, 2006; Preti, Rocchi, Sisti, Camboni, & Miotto, 2011) and a robust and differential pattern of comorbidity has been found between BPD and BN in clinical samples of mixed personality disorder (PD; Reas, Rø, Karterud, Hummelen, & Pedersen, 2013). Significant associations have also been found between purging behavior, personality traits, and self-injury or suicidality in women with eating disorders (ED) (Paul, Schroeter, Dahme, & Nutzinger, 2002) and adolescent psychiatric inpatients (Zaitsoff & Grilo, 2010), consistent with clinical observations linking BN with impulsivity and dysregulation.
The extent to which BN in the presence of BPD might confer additional risk of suicidality and self-injury is unclear. Several studies have investigated the predictive validity of comorbidity associated with suicide risk among individuals with PD, yet these have focused largely on risk attributable to major depression, substance abuse, or anxiety disorders (Soloff, Lynch, Kelly, Malone, & Mann, 2000; Wedig et al., 2012; Yen et al., 2003), with comparatively scarce attention on the independent predictive utility of BN. To our knowledge, only two controlled investigations have specifically addressed the incremental prognostic validity of BN in samples of BPD sufferers (Chen, Brown, Harned, & Linehan, 2009; Dulit, Fyer, Leon, Brodsky, & Frances, 1994). An investigation by Dulit et al. (1994) found that BPD inpatients (N = 124) with comorbid BN were four times more likely to engage in repeated self-injury (≥5 lifetime acts) than BPD inpatients without BN. An investigation of 135 women with BPD observed a significant association between BN and recurrent suicide attempts (≥2 acts), but not other self-injury, after controlling for age and other non-ED Axis I disorders (Chen et al., 2009). A later study by Chen et al. (2011) found equivalent rates of lifetime suicide attempts and self-injury among 166 women and 166 men with and without ED in a diagnostically heterogeneous sample of PD.
Evidence on a cross-sectional level supporting the incremental validity of BN in predicting suicidality in this high-risk population would underscore the importance of screening for BN in treatment settings for BPD. Such an approach is also consistent with recommendations that future researchers consider the effects of comorbidity when elucidating predictive effects of mental disorders on suicide attempts (Nock, Hwang, Sampson, & Kessler, 2010). To be of greatest benefit, further investigations should address previously identified methodological limitations, including (a) the merging of highly select samples from trials recruiting or screening specifically for suicidal BPD or substance-dependent BPD, which may limit ecological or clinical representativeness, and (b) small and/or diagnostically heterogeneous samples which may render effect sizes diminished due to low power. The present study aimed to investigate whether comorbidity-independent associations exist between BN and self-harm, suicidal ideation, and suicide attempts among a naturalistic, treatment-seeking sample of women with BPD.
Method Participants
Participants included 483 women diagnosed with BPD, aged 18–65 years, with an initial admission between 1996 and 2009 to the Norwegian Network of Personality-Focused Treatment Programs. Established in 1992, this is a clinical research network providing mostly long-term, group-based (or concurrent individual–group) treatment (see also Karterud et al., 2003; Reas et al., 2013). All treatment units used uniform and standardized assessment procedures (Pedersen, Karterud, Hummelen, & Wilberg, 2013) following the longitudinal, expert, all-data (LEAD) standard, which is a comprehensive, integrative diagnostic approach using multiple sources of information (e.g., interview data, informants, behavioral observations, and medical records). Data collection is overseen by a central coordinating site responsible for quality assurance, standardization of routines, and screening data for irregularities and missing data. Raters all held professional degrees and skill acquisition, and maintenance included training courses and supervision. Ongoing monitoring of protocol adherence included checklists and periodic site visits (up to 3–4 times annually) and in addition, site coordinators from all 16 units met every 6 months to discuss and calibrate diagnostic and clinic procedures.
Materials and Procedure
Patients were interviewed with the Structured Clinical Interview for DSM–IV Axis II Personality Disorders (SCID-II; First, Gibbon, Spitzer, Williams, & Benjamin, 1997), a well-established diagnostic tool with demonstrated reliability for the assessment of PD (Lobbestael, Leurgans, & Arntz, 2011). Interrater reliability for the Norwegian version of the SCID-II has been established (κ = .66 for BPD; Kvarstein et al., 2014). The Mini-International Neuropsychiatric Interview Version 4.4 (M.I.N.I.; Sheehan et al., 1998) was used to establish Axis I diagnoses, which has demonstrated reliability and validity for the assessment of Axis I disorders, including BN (κ = .78; Sheehan et al., 1998). The Norwegian version of the M.I.N.I is validated and is considered a time-efficient and feasible alternative to the SCID-P (SCID-I/P; First et al., 2002) and CIDI (CIDI; Kessler & Ustün, 2004) (Mordal, Gundersen, & Bramness, 2010).
Assessment of Suicidal Behavior
The assessment of suicidality included clinician- and self-reported data capturing different time epochs. First, self-reported data were systematically collected at intake using a routinely administered sociodemographic questionnaire. Suicidal ideation was assessed dichotomously to capture the past 7 days and past 12 months, that is, “Have you had thoughts about taking your own life?” Self-harm behavior was assessed dichotomously to capture the past 12 months and lifetime, that is, “Have you [ever] physically hurt yourself, for example, cutting, scratching, burning, head-banging, and so forth?” Suicide attempts were assessed dichotomously to capture the past 12 months and lifetime, that is, “Have you ever tried to kill yourself?” The number of lifetime suicide attempts was also rated. Clinician-rated data were systematically collected at discharge using a routine discharge form to assess (a) the occurrence of self-harm behavior during treatment, that is, physically harmful behaviors such as cutting, scratching, burning, banging against hard objects, and so forth; (b) suicidal ideation during treatment, that is, “Did the patient express thoughts about taking one’s life, excluding very fleeting or dramatic expressions regarding the wish to die?” and (c) suicide attempts occurring during treatment, that is, “lethal acts with the intent to die that would have been successful without acute intervention from others.” It should be noted that assessment of self-harm behavior did not specify expectations (e.g., to gain relief from negative feelings, relieve suffering, resolve interpersonal difficulties) as specified in DSM-5 (APA, 2013) for nonsuicidal self-injury; thus, we use the more general term of self-harm. Bulimic behaviors, sometimes conceptualized under the rubric of self-harm, were not covered by assessment. All data were registered in a central, anonymous database administered by Oslo University Hospital. All patients provided written consent and the study was approved by the State Data Inspectorate and the Regional Ethics Committee.
Data Analyses
Analyses were conducted using predictive analysis software (PASW) Version 18.0. Patients were grouped according to the presence of BN (BPD-BN) or non-ED Axis I disorder (BPD-other), in line with grouping methods by Chen et al. (2011). Cases of anorexia nervosa (AN) (N = 7) and eating disorders not otherwise specified (EDNOS) (N = 81) were excluded from BPD-other due to potential confounding of subthreshold BN in the EDNOS group, or lifetime history of BN, because diagnostic fluctuation or crossover is common in DSM-IV ED (Peterson et al., 2011). Chi-square analyses tested differences for categorical variables. Consistent with methods from earlier studies (Bodell, Joiner, & Keel, 2013), logistic regression analyses (ORs and 95% CIs) controlled for mood, anxiety, and substance-use disorders were conducted when significant bivariate associations were detected. Following guidelines detailed in previous research (Chen, Cohen, & Chen, 2010), ORs of 1.68, 3.47, and 6.71 were considered equivalent to small, medium, and large effect sizes (Cohen’s d = 0.2, 0.5, and 0.8, respectively). Analyses were two-tailed (p < .05).
Results Sample Characteristics
A total of 57 patients (11.8%) received a comorbid diagnosis of BN and BPD and were grouped as BPD-BN. All other patients (N = 426) were diagnosed with at least one non-ED Axis I diagnosis (BPD-other). Approximately 68% of both groups had mood disorders; 63.2% versus 52.6% (p = .123) were diagnosed with anxiety disorder; and 28.1% versus 15.1% (p = .014) had substance-use disorder. Table 1 shows no significant baseline differences for age, mean global assessment of functioning (GAF) at intake, length of treatment, mean frequency of non-ED Axis I or Axis II disorders, and number of SCID-II BPD criteria fulfilled. No differences were detected for marital status, χ2(4, 459) = 2.97, p = .562, or work situation, χ2(5, 421) = 4.91, p = .427.
Sample Characteristics for BPD-BN Versus BPD-Other (N = 483)
Clinician and Self-Reported Self-Harm, Suicidal Ideation, and Suicide Attempts
As shown in Table 1, a significantly greater proportion of women in the BPD-BN group demonstrated self-harm behavior during treatment (p < .001). Approximately 50% of both groups engaged in self-harm behavior over the past 12 months. Lifetime self-harm did not show a statistically significant difference between BPD-BN and BPD-other (70.9% vs. 58.2%; p = .079). A significantly higher proportion of BPD-BN reported suicidal ideation during the past 7 days prior to intake (p = .012), but differences in suicidal ideation (past 12 months) were not significant (88.4% vs. 77.4%, p = .101). A trend was detected for greater suicidal ideation during treatment in BPD-BN (p = .058).
A significantly higher proportion of women with BPD-BN attempted suicide during treatment (p = .029). No significant differences were found for suicide attempts during the past 12 months (31.8% vs. 22.8%, p = .192), lifetime, or recurrent suicide attempts (two or more acts). For suicide attempters, mean (SD) number of lifetime attempts was 3.0 (2.24) for BPD-BN versus 2.80 (2.68) for BPD-other, respectively, t(255) = .401, p = .688.
Logistic Regression
Table 2 shows significant associations between BN and self-harm during treatment OR = 3.23, 95% CI [1.76–5.92], suicidal ideation past 7 days, OR = 2.37, 95% CI [1.23–4.55]; and suicide attempts during treatment OR = 2.83; 95% CI [1.05–7.64]. An OR of 1.73, 95% CI [.987–3.04], p = .055 was detected for suicidal ideation during treatment, indicating a small effect.
Logistic Regression Models for the Association Between BN and Self-Harm, Suicidal Ideation, and Suicide Attempts Among Women With BPD Controlling for Mood, Anxiety, and Substance-Use Disorders
DiscussionThese findings indicate that the presence of a comorbid diagnosis of BN in the context of BPD is significantly and uniquely associated with increased risk of recent suicidal ideation at intake and self-harm and suicide attempts during treatment after controlling for mood, anxiety, and substance-related disorders. As such, a concurrent diagnosis of BN among women seeking treatment for BPD appears to represent a strong and significant marker for immediate life-threatening behaviors. Death by suicide occurs in 8–10% of individuals with BPD, which is among the highest rates of all mental disorders (Pompili, Girardi, Ruberto, & Tatarelli, 2005). Our study has indicated that bulimic episodes signal even greater risk of suicide attempts within this high-risk population. Findings echo those by Bodell et al. (2013), who found comorbidity-independent associations between BN and lifetime suicidality among university women, prompting calls for a standard risk assessment of suicide among women with BN.
The sample size and setting were considered study strengths, that is, over 400 consecutively admitted female patients seeking day treatment for BPD. No significant differences were observed in length of admission (i.e., approximately 4.5 months) or length of referral process; thus, these variables were not considered potential confounds. Data collection was part of routine clinic procedure, and although risk of bias cannot be eliminated, only minimal bias owing to clinician expectations regarding the present study aims were expected to influence results.
Several limitations of our study are important to consider. Despite face validity, the reliance on direct, single-item assessments limited the scope of the measurement. Information regarding lethality, intent, or specific methods of self-harm was unavailable, and behaviors such as aborted, interrupted, and low-lethality attempts might have not been captured. Prior research, however, has used single-item, self-report assessments of suicidal ideation and attempt with demonstrated validity (Bodell et al., 2013; Cougle, Keough, Riccardi, & Sachs-Ericsson, 2009). Retrospective self-report data on the occurrence of self-harm and suicidal behavior might be subject to recall bias. This study design was cross-sectional, thereby precluding the ability to infer stability of findings and the longitudinal risk or other clinical outcomes (e.g., suicide completion). Self-harm behavior, suicidal ideation, and past suicide attempts have been documented as important risk factors for future suicide (Klonsky, May, & Glenn, 2013), also in samples with BPD (Wedig et al., 2012). The overall rate of lifetime attempts in our sample was 59%, which is higher than typically observed in BN (25–35%; Franko & Keel, 2006), although within the 40–85% range observed for BPD (Oumaya et al., 2008). Our sample included treatment-seeking women admitted for intensive and specialized day-hospital treatment for PD; thus, results may not generalize to individuals under 18 or over 65 years of age, or to community or nonspecialist treatment settings which serve less severe populations.
Because the pattern of findings indicated significantly elevated risk of suicidality at intake and during treatment, but not prior to treatment, replication is necessary to rule out potential state effects underlying results. Further investigation is needed to explore whether findings relate to appropriateness of therapeutic setting or treatment approach, for example, or whether suicidality might constitute a particularly salient motivator for treatment-seeking among women with BPD-BN. Several alternative classification schemes for subtyping BN according to patterned heterogeneity in comorbidity (i.e., multi-impulsive BN, borderline–nonborderline BN, undercontrolled–externalizing; see Wildes & Marcus, 2013) may have relevance for the contextualization and conceptualization of findings, or our results may speak broadly to cross-cutting behavioral and neurobiologically informed constructs such as trait impulsivity or cognitive control (Insel, 2014).
This was a controlled investigation providing evidence on a cross-sectional level supporting the incremental validity of BN in predicting suicidality beyond mood, anxiety, and substance-related disorders in the high-risk BPD population. Findings warrant routine assessment of BN in treatment-seeking samples of women with BPD, and underscore the importance of high vigilance and fastidious monitoring of suicidal behaviors during treatment for these individuals.
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Submitted: June 6, 2014 Revised: October 29, 2014 Accepted: October 31, 2014
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Source: Journal of Consulting and Clinical Psychology. Vol. 83. (3), Jun, 2015 pp. 643-648)
Accession Number: 2014-55558-001
Digital Object Identifier: 10.1037/ccp0000014
Record: 47- Title:
- Suicide attempts in a longitudinal sample of adolescents followed through adulthood: Evidence of escalation.
- Authors:
- Goldston, David B.. Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, US, david.goldston@duke.edu
Daniel, Stephanie S.. Center for Youth, Family, and Community Partnerships, University of North Carolina at Greensboro, NC, US
Erkanli, Alaattin. Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, US
Heilbron, Nicole. Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, US
Doyle, Otima. Jane Addams College of Social Work, University of Illinois, Chicago, IL, US
Weller, Bridget. Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, US
Sapyta, Jeffrey. Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, US
Mayfield, Andrew. Center for Youth, Family, and Community Partnerships, University of North Carolina at Greensboro, NC, US
Faulkner, Madelaine. Center for Youth, Family, and Community Partnerships, University of North Carolina at Greensboro, NC, US - Address:
- Goldston, David B., Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, 2608 Erwin Road, Suite 300, DUMC 3527, Durham, NC, US, 27710, david.goldston@duke.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 83(2), Apr, 2015. pp. 253-264.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 12
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- adolescence, sensitization, escalation, suicide attempts, developmental trends
- Abstract (English):
- Objectives: This study was designed to examine escalation in repeat suicide attempts from adolescence through adulthood, as predicted by sensitization models (and reflected in increasing intent and lethality with repeat attempts, decreasing amount of time between attempts, and decreasing stress to trigger attempts). Method: In a prospective study of 180 adolescents followed through adulthood after a psychiatric hospitalization, suicide attempts, and antecedent life events were repeatedly assessed (M = 12.6 assessments, SD = 5.1) over an average of 13 years 6 months (SD = 4 years 5 months). Multivariate logistic, multiple linear, and negative binomial regression models were used to examine patterns over time. Results: After age 17–18, the majority of suicide attempts were repeat attempts (i.e., made by individuals with prior suicidal behavior). Intent increased both with increasing age, and with number of prior attempts. Medical lethality increased as a function of age but not recurrent attempts. The time between successive suicide attempts decreased as a function of number of attempts. The amount of precipitating life stress was not related to attempts. Conclusions: Adolescents and young adults show evidence of escalation of recurrent suicidal behavior, with increasing suicidal intent and decreasing time between successive attempts. However, evidence that sensitization processes account for this escalation was inconclusive. Effective prevention programs that reduce the likelihood of individuals attempting suicide for the first time (and entering this cycle of escalation), and relapse prevention interventions that interrupt the cycle of escalating suicidal behavior among individuals who already have made attempts are critically needed. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Impact Statement:
- What is the public health significance of this article?—Some individuals attempt suicide on multiple occasions during adolescence and young adulthood. As they make repeated attempts, the severity of their intention to die increases, and the amount of time between their suicide attempts decreases on average. These findings underscore the need for effective interventions to prevent and interrupt this cycle of escalation in suicidal behavior. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Attempted Suicide; *Sensitization; *Trends
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Child; Female; Humans; Intention; Longitudinal Studies; Male; Prospective Studies; Suicidal Ideation; Suicide; Suicide, Attempted; Young Adult
- PsycINFO Classification:
- Behavior Disorders & Antisocial Behavior (3230)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Childhood (birth-12 yrs)
School Age (6-12 yrs)
Adolescence (13-17 yrs)
Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs) - Tests & Measures:
- Interview Schedule for Children and Adolescents
Follow-Up Interview Schedule for Adults
Beck Suicide Intent Scale
Lethality of Suicide Attempt Rating Scale
Life Events Checklist
Subjective Intent Rating Scale DOI: 10.1037/t11293-000 - Grant Sponsorship:
- Sponsor: National Institutes of Health, National Institute of Mental Health
Grant Number: R01MH048762; K24MH066252
Recipients: No recipient indicated - Methodology:
- Empirical Study; Longitudinal Study; Prospective Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Jan 26, 2015; Accepted: Nov 12, 2014; Revised: Oct 31, 2014; First Submitted: Feb 6, 2013
- Release Date:
- 20150126
- Correction Date:
- 20160512
- Copyright:
- American Psychological Association. 2015
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0038657
- PMID:
- 25622200
- Accession Number:
- 2015-02672-001
- Number of Citations in Source:
- 85
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-02672-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-02672-001&site=ehost-live">Suicide attempts in a longitudinal sample of adolescents followed through adulthood: Evidence of escalation.</A>
- Database:
- PsycINFO
Suicide Attempts in a Longitudinal Sample of Adolescents Followed Through Adulthood: Evidence of Escalation
By: David B. Goldston
Duke University School of Medicine;
Stephanie S. Daniel
University of North Carolina at Greensboro
Alaattin Erkanli
Duke University
Nicole Heilbron
Duke University School of Medicine
Otima Doyle
University of Illinois, Chicago
Bridget Weller
Duke University School of Medicine
Jeffrey Sapyta
Duke University School of Medicine
Andrew Mayfield
University of North Carolina at Greensboro
Madelaine Faulkner
University of North Carolina at Greensboro
Acknowledgement: David B. Goldston, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine; Stephanie S. Daniel, Center for Youth, Family, and Community Partnerships, University of North Carolina at Greensboro; Alaattin Erkanli, Department of Biostatistics and Bioinformatics, Duke University; Nicole Heilbron, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine; Otima Doyle, Jane Addams College of Social Work, University of Illinois, Chicago; Bridget Weller and Jeffrey Sapyta, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine; Andrew Mayfield and Madelaine Faulkner, Center for Youth, Family, and Community Partnerships, University of North Carolina at Greensboro.
Research reported in this publication was supported by the National Institute of Mental Health of the National Institutes of Health (R01MH048762 and K24MH066252). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
The rate of suicide attempts varies over the life span. For example, in community and population-based studies, the transition to adolescence has been found to be associated with a marked increase in the rates of suicide attempts (Boeninger, Masyn, Feldman, & Conger, 2010; Joffe, Offord, & Boyle, 1988; Kessler, Borges, & Walters, 1999; Lewinsohn, Rohde, Seeley, & Baldwin, 2001; Velez & Cohen, 1988; Wunderlich et al., 2001). Females generally attempt suicide at higher rates than males (Lewinsohn et al., 2001; Nock et al., 2013), and in one study, the increase in suicide attempts during adolescence was found primarily among females (Lewinsohn et al., 2001). In community samples, the rate of suicide attempts has been observed to decline during the transition to young adulthood (Kessler et al., 1999; Lewinsohn et al., 2001). Fewer studies have examined age trends in suicide attempts in clinical or high-risk samples, but results suggest similar patterns in the prevalence of suicidal behavior (Angle, O’Brien, & McIntire, 1983; Kovacs, Goldston, & Gatsonis, 1993).
Despite decreasing rates of suicide attempts from adolescence through early adulthood, a greater proportion of the attempts that do occur may be made by individuals who have attempted suicide on more than one occasion. For example, within 3 to 5 years of previous suicidal behavior, rates of repeat suicide attempts among adolescents and adults who have presented in treatment settings range from 25% to 31% (Christiansen & Jensen, 2007; Goldston et al., 1999; Tejedor, Diaz, Castillon, & Pericay, 1999). Moreover, past suicide attempts have been found to be strongly associated with increased risk for future attempts (Goldston et al., 1999; Leon, Friedman, Sweeny, Brown, & Mann, 1989). Hence, adolescents who have attempted suicide may be at particularly high risk for repeat attempts even as they transition into adulthood.
The increased risk for suicidal behavior among individuals with prior suicide attempts may be in part because of sensitization processes. If such processes were operative, individuals would become more sensitive to and show increased reactivity to the triggers for behaviors or illness with repeated exposures to those triggers (Post, Rubinow, & Ballenger, 1986; Post, 2007). Sensitization processes are reflected in three patterns of response. First, when there is sensitization, individuals become more reactive with repeated exposures, and the magnitude of the behavioral or physiological response to the stress increases in intensity. In the example of suicide attempts, the severity of the suicide attempts (e.g., intent and/or lethality of attempts) would increase with repetition. Second, with increased sensitivity to triggers or precipitants, there are more rapid recurrences of the behavioral response or episode of disorder. For suicide attempts, this would be reflected in a decreasing amount of time between repeated suicide attempts. Third, with repeated exposure to a provocative stimulus or stress, it takes progressively less severe stress to trigger or provoke the reaction. In the case of a suicidal person, the amount of life stress that could trigger a suicide attempt would decrease as the individual made an increasing number of attempts.
Sensitization processes have been posited to be associated with different psychiatric disorders, including affective disorders (Bender & Alloy, 2011; Monroe & Harkness, 2005; Post et al., 1986; Post, 1992). For example, earlier episodes of affective disorder in adults are often less severe than later episodes (Kessing, 2008; Lewinsohn, Zeiss, & Duncan, 1989; Maj, Veltro, Pirozzi, Lobrace, & Magliano, 1992). Several (but not all) studies have found that among adults, there is a decreasing amount of time between affective disorder episodes as the number of episodes increases (Kessing, 1998). Furthermore, in adults and older adolescents, recurrent episodes of affective illness are sometimes precipitated by less severe stresses than initial or earlier episodes (Bender & Alloy, 2011; Monroe & Harkness, 2005; Post, 1992; Stroud, Davila, Hammen, & Vrshek-Schallhorn, 2011).
Despite these findings with affective disorders, research findings have been inconsistent regarding whether recurrent suicide attempts conform to a pattern that would be consistent with a sensitization model. Intent and medical lethality, for example, are often considered to be indices of severity of suicide attempts. To date, there have been mixed findings from studies of adults regarding associations between these indices and patterns of suicide attempts. Specifically, some studies have found that repeat suicide attempts are associated with greater intent to die than first-time suicide attempts (Kaslow et al., 2006; Reynolds & Eaton, 1986), whereas other studies have not found this pattern (Forman, Berk, Henriques, Brown, & Beck, 2004; Michaelis et al., 2003; Ojehagen, Danielsson, & Traskman-Bendz, 1992). Similarly, there have been mixed findings regarding a possible association between higher medical lethality and increasing number of suicide attempts (Forman et al., 2004; Kaslow et al., 2006; Michaelis et al., 2003; Pettit, Joiner, & Rudd, 2004; Reynolds & Eaton, 1986). No studies, to our knowledge, have examined the possibility that there may be decreasing amounts of time between successive suicide attempts. Last, the studies of life stress among individuals with differing numbers of prior suicide attempts have yielded mixed findings. For example, the amount of life stress preceding a suicide attempt has variously been found to not differ between individuals making their first attempts and individuals making repeat attempts (Crane et al., 2007; Joiner & Rudd, 2000; Kaslow, Jacobs, Young, & Cook, 2006; Pompili et al., 2011), to be positively related to the number of past attempts (Pettit et al., 2004), and to be related to severity of suicidal episode among individuals making first but not repeat attempts (Crane et al., 2007; Joiner & Rudd, 2000). Putting these mixed findings in context, it is worth noting that with few exceptions (e.g., Joiner & Rudd, 2000; Ojehagen et al., 1992), the majority of studies pertinent to sensitization models of suicide attempts have been cross-sectional in nature and focused on individuals at treatment entry. Cross-sectional comparisons of suicide attempts that precede initiation of treatment may be biased because these attempts may not be typical of all attempts. Prospective studies of recurrent suicide attempts within the same individuals over significant periods of time may be less biased, and more likely to reflect escalation of suicidal behavior consistent with sensitization models.
Sensitization processes are prominently described in theoretical conceptualizations of suicidal behavior. For example, Joiner (2005), in describing the factors that may contribute to increasing courage for suicide, referred to the process of cognitive sensitization. This occurs when “[an individual] undergoes a provocative experience, and subsequently, images and thoughts about that experience become more accessible and easily triggered . . . As suicidal experience accumulates, suicide-related cognitions and behaviors may become more accessible and active. The more accessible and active these thoughts and behaviors become, the more easily they are triggered (even in the absence of negative events), and the more severe are the subsequent suicidal episodes” (pp. 82–83). To this point, Beck (1996) theorized that the cognitive schemas underlying information processing become integrated with motivational, behavioral, and affective response systems. With repeated exposure to relevant experiences, these “modes” of responding, including a hopeless-suicidal mode, become more accessible and more easily activated. As a result, more severe reactions can result from less serious precipitants. Sensitization conceptualizations have been highly influential in our current thinking about suicidal behavior and in the development of interventions for suicidal individuals (e.g., Brown et al., 2005). However, the suggestions that sensitization processes might account in part for patterns of recurrence of suicidal behavior have not previously been tested among individuals followed over long periods.
If sensitization processes contribute to the recurrence of suicidal behavior, a sensitization model would provide a framework for understanding the course and repetition of suicidal behavior (Post et al., 1986, 1992). It also would provide a framework for understanding the high-risk group of individuals who have made multiple suicide attempts, and whose suicidal behavior has become increasingly more severe over time (Post et al., 1986). A sensitization model additionally would have implications for relapse prevention approaches for working with suicidal individuals, and for the theoretical conceptualizations that provide the basis for these interventions (Segal et al., 1996).
In 1991, we began conducting a naturalistic, prospective study of the risk for suicidal behaviors among adolescents who were psychiatrically hospitalized and then followed through young adulthood. With repeated assessments, we examined patterns in suicide attempts in adolescence and through adulthood after hospitalization, and also retrospectively assessed suicide attempts before hospitalization. This continuous record of suicidal behavior allowed us a rare opportunity to examine the degree to which a sensitization model might account for patterns in recurrent attempts across two developmental periods (adolescence and young adulthood). We hypothesized that (a) the severity of suicidal behavior, as reflected in intent to die and in the medical lethality of suicide attempts, would increase as the number of suicide attempts by an individual increases; (b) there would be decreasing amounts of time between successive pairs of suicide attempts as the number of suicide attempts made by an individual increases, and (c) the degree of association between severe life stresses and suicide attempts would decrease as the number of suicide attempts by an individual increases.
Method Participants and Overview of Procedures
The 180 participants in this study were followed prospectively from adolescence, when they were psychiatrically hospitalized, through young adulthood. To be eligible for the study, youths needed to be: (a) 12–19 years old at index hospitalization, (b) hospitalized for 10 or more days, (c) able to cooperate with and complete the assessments in the hospital, and (d) a resident of North Carolina or Virginia at time of recruitment. Adolescents were excluded from the study if they (a) had a serious physical disease, (b) had intellectual disability, or (c) if their sibling was already enrolled in the study. At the time the study was initiated, the average length of stay in hospitals was 23.6 days (National Association of Psychiatry Health Systems, 2002). Hence, the stipulation of hospital stays of 10 or more days was made because patients with shorter hospital stays were often considered by clinical staff to have less severe problems or to be inappropriate for hospitalization. For example, adolescents with shorter lengths of stays had lower scores on the Beck Depression Inventory than individuals with longer stays (Goldston et al., 1999).
Patients on the inpatient unit participated in a comprehensive intake assessment as part of their psychiatric evaluations, including psychiatric diagnostic interviews and interviews about prior suicidal behavior. To recruit the longitudinal sample, we attempted to contact individuals (and their parents/guardians) who met inclusion and exclusion criteria ∼6 to 8 months after discharge from the hospital. Adolescents and their parents or guardians were contacted in the order of their discharge from the hospital. The total eligible sample consisted of 225 adolescents and their parents or guardians. One adolescent died of cardiac problems before we were able to contact him. We contacted 96% of the remaining sample and of these, 84% (n = 180) agreed to participate. The final sample consisted of 91 girls and 89 boys; 80% were European American, 16.7% were African American, and the other participants were Hispanic American, Native American, or Asian American. The average age of participants was 14 years 10 months (SD = 1 year, 7 months; range = 12 years 0 months to 18 years 5 months) at their index hospitalization. Sixteen percent of youths were in the custody of the Department of Social Services at study entry. For the remaining families, the socioeconomic status as classified by the Hollingshead (1957) index from highest to lowest was as follows: I = 3.3%, II = 12.6%, III = 21.9%, IV = 29.8%, and V = 32.4%. At the time of their index hospitalization, 41.7% (n = 75) of the youths had histories of suicide attempts and another 33.3% (n = 60) reported current suicide ideation (Goldston et al., 1999). Psychiatric disorders at the index hospitalization and over the course of the longitudinal study, and the relationship of these psychiatric disorders to risk for suicide attempts have previously been described (Goldston et al., 1999, 2009).
The design of the follow-up study called for the participants to have their first follow-up assessment 6 to 8 months after hospitalization. After their initial assessment in the study, this schedule was tapered so that assessments were subsequently scheduled every 10 to 12 months, and then annually. The longitudinal methods for this study were modeled after successful longitudinal studies by Kovacs and colleagues, in which the time between follow-up assessments after the initial assessments was also tapered (Kovacs, Feinberg, Crouse-Novak, Paulauskas, & Finkelstein, 1984; Kovacs, Obrosky, Goldston, & Drash, 1997). The more frequent assessments at the beginning of the study allowed us to more closely track the course of psychiatric problems after the hospitalization. The amount of time between assessments was tapered as a practical consideration to reduce burden on participants and to reduce study costs.
The median amount of time preceding the first three follow-up assessments ranged from 8.2 to 10.1 month, whereas the median time preceding assessments 8, 9, and 10 ranged from 10.9 to 11.4 months. The number of assessments and the amount of time between assessments varied both within and across participants because of scheduling conflicts, subject requests, staff shortages, funding lapses, and difficulties locating or contacting participants. These assessments occurred primarily in the homes of participants, but also at a university or medical center, in jails and prisons, or in other settings convenient to participants. A variety of methods were used to maintain contact with the sample including phone calls and correspondence, maintenance of information regarding ancillary contacts, use of publicly available databases to help locate participants, and scheduling of assessments in participants’ homes and communities.
As of June 30, 2009, participants had been followed for a maximum of 17.5 years (M = 13 years 6 months; SD = 4 years 5 months), and participated in a total of 2,270 assessments, including the baseline hospital assessments (M = 12.6 assessments, SD = 5.1, range = 2 to 26). The mean age of participants at the last assessment was 28 years 5 months (SD = 4 years 10 months; range = 13 years 0 months to 34 years, 7 months). By the cutoff date for this article, 20 individuals had dropped out of the study, 6 participants had been administratively withdrawn from the study because of lost contact, and 8 participants had died (none because of suicide). Six of the individuals who were no longer active in the study made posthospitalization attempts.
The subsamples of participants used in analyses of developmental trends and to test the different hypotheses are described in Table 1. For developmental trends, we focused on the 109 participants who attempted suicide at least once in their lives, either before hospitalization or during the follow-up study. Of note, 34 of the 105 (32.3%) participants who had not attempted suicide by the time of their index hospitalization subsequently attempted suicide (total attempts = 65, M = 1.9, SD = 1.4, range = 1 to 6) over the follow-up. To test the hypothesis regarding intent and lethality as a function of number of suicide attempts, we focused on the 41 participants who made more than one suicide attempt at any point over the follow-up or during the 2 weeks before hospitalization. The decision to not examine data regarding intent and lethality of suicide attempts before the 2 weeks that preceded the index hospitalization was made in an effort to reduce potential bias in retrospective reports of clinical characteristics. For the hypothesis regarding the amount of time between suicide attempts, we focused on the 63 participants who had a lifetime history of repeat attempts. Last, to test the hypothesis regarding the association between life events and suicide attempts, we focused on the 36 individuals who made more than one suicide attempt after their discharge from the hospital. This strategy was used because life events were assessed only posthospitalization.
Characteristics of Samples for Examination of Developmental Trends and for Tests of the Sensitization Hypotheses
Research interviewers were master’s and doctoral level mental health professionals. The interviewers were extensively trained (e.g., with role plays, calibration of symptom ratings, and observed interviews) and supervised by the principal investigators for the study (D.G., S.D.).
The institutional review boards of the participating institutions provided approval for this ongoing study, and for use of clinical data from the baseline hospitalization for research purposes. Participants provided assent and their parents or legal guardians provided consent at the time of the hospitalization. Participants who turned 18 while participating in the study provided consent at the next assessment following their 18th birthday. Participants were reconsented an additional time at the beginning of the last funding period for the grant.
Instruments
Assessment of suicide attempts
The Interview Schedule for Children and Adolescents (ISCA; Kovacs, Pollock, & Krol, 1997; Sherrill & Kovacs, 2000) and the Follow-Up Interview Schedule for Adults (FISA; Kovacs, Pollock, & Krol, 1995; Sherrill & Kovacs, 2000) are semistructured clinical interviews developed for longitudinal studies used to assess symptoms of psychiatric disorders. Psychiatric diagnoses obtained with these instruments have been shown to be reliable and to have predictive validity as summarized by Sherrill and Kovacs (2000). In the current investigation, these instruments were used to assess suicide attempts. To aid in this assessment, the ISCA and FISA have standardized questions about the presence/absence of thoughts of death, suicide ideation, and suicide attempts, plans and methods, circumstances and suicidal motivations, and psychological intent (e.g., “Have you ever thought about killing yourself?” “Have you ever done anything to try to kill yourself?” “What did you do?” “What did you think would happen when you ____?”). In these instruments, suicide attempts were defined operationally as potentially self-injurious behaviors associated with some (i.e., nonzero) intent to end one’s life; this definition is consistent with current approaches to classification of suicide-related terms (Crosby, Ortega, & Melanson, 2011; Posner, Oquendo, Gould, Stanley, & Davies, 2007; Silverman, Berman, Sanddal, O’Carroll, & Joiner, 2007). Self-injurious behaviors not associated with at least some intent to kill oneself (e.g., cutting to relieve tension) were not considered as suicide attempts. If reports of self-harm were vague or indefinite (e.g., “I honestly can’t remember what was going through my head,” “she took a bunch of pills but I have no idea if she was trying to kill herself or just get high”), the behavior conservatively was not counted as a suicide attempt.
At the index hospitalization and over the follow-up period, all available information was used to make determinations of the dates of attempts. Sources of information included the semistructured interviews; treatment, legal, and school records; and parent interviews. At the index hospitalization, we obtained information about all previous suicide attempts. In subsequent assessments, we assessed all suicide attempts since last contact. The information obtained at the index hospitalization and follow-up assessments was combined to yield continuous (lifetime) records of participants’ suicidal behavior. The ISCA was used in interviews with adolescents at hospitalization, and in interviews with parents or guardians and adolescents over the follow-up until participants reached the age of 18 or began living independently. After that point, the participants were administered the FISA, but parents and guardians were not interviewed.
When participants could not provide precise dates for suicide attempts, but could describe a likely window of time during which the attempt occurred, the dates were estimated as the midpoint of the defined period of time (Kovacs, Feinberg, Crouse-Novak, Paulauskas, & Finkelstein, 1984). Before the age of 18, suicide attempts (meeting our operational definition of this behavior) were considered to be present when reported by either adolescent or parent. The strategy of counting suicide attempts as present when reported by either adult informants or adolescent participants was used in light of the findings from multiple studies that parents are often not aware of adolescents’ suicide attempts (e.g., Breton, Tousignant, Bergeron, & Berthiaume, 2002; Foley, Goldston, Costello, & Angold, 2006; Walker, Moreau, & Weissman, 1990).
We have conducted two interrater reliability trials of our classifications of suicidal thoughts and behavior in this sample using all information, including interviews with the ISCA and FISA, and treatment records. In the first trial of 40 cases, there was 95.0% agreement in the classification of suicide ideation and suicide attempts (Goldston et al., 2001). In a second trial, 500 cases were classified as to whether there was presence of (a) no suicide ideation, (b) suicide ideation without means envisioned, (c) suicide ideation with means envisioned, (d) a single suicide attempt, or (e) multiple attempts since the last assessment. In this trial, there was excellent agreement between previously determined consensus ratings and the ratings of an independent coder (96.4% agreement; κ = 0.92). In all cases, discrepancies in ratings were discussed and resolved by consensus.
Assessment of suicide intent
The subjective intent of suicide attempts during the follow-up period was assessed on the basis of all available information, using the 4-point Subjective Intent Rating Scale developed by our research group (SIRS; Sapyta et al., 2012). This scale was developed to assess suicide intent independently of related constructs such as impulsivity or factors potentially related to medical lethality such as isolation at the time of the attempt. The construct validity of the SIRS has been demonstrated by the higher correlation with the Subjective index than with the Objective index of the Beck Suicide Intent Scale (Beck, Schuyler, & Herman, 1974). Based on all available information including responses to the ISCA and FISA (Sherrill & Kovacs, 2000), intent was rated from “Mild” (respondent acknowledges a wish to die, but mainly wants to live) to “Very High” (respondent expresses very little ambivalence about wanting to die). There was not a point on this scale corresponding to no intent, because by definition, suicide attempts were associated with at least some intent to die. Two independent coders rated intent, and discrepancies were resolved by consensus. SIRS ratings in this study have been found to have high interrater reliability (ICC = 0.99, p < .05), and the maximum intent of past suicide attempts has been found to be predictive of future attempts (Sapyta et al., 2012). The average unweighted intent score for suicide attempts among participants that made more than one attempt was 2.51 (SD = 0.93).
Assessment of medical lethality
Medical lethality of all suicide attempts during the follow-up was rated on the basis of all available information using the Lethality of Suicide Attempt Rating Scale (Berman, Shepherd, & Silverman, 2003; Smith, Conroy, & Ehler, 1984). Using this scale, the suicide attempts were rated in severity of potential medical consequences from 0 (death is an impossibility) to 10 (death is almost a certainty) by two independent raters, with discrepancies resolved by consensus. This scale has been shown to have high interrater reliability and concurrent validity among adolescents as well as adults (Lewinsohn, Rohde, & Seeley, 1996; Nasser & Overholser, 1999; Sapyta et al., 2012) and the maximum lethality of past suicide attempts was found to be predictive of future suicidal behavior (Sapyta et al., 2012). In this sample, there was high interrater reliability in ratings from this scale (ICC = 0.95, p < .05; Sapyta et al., 2012). Similar to other clinical and epidemiologic samples of young people (e.g., Diamond et al., 2005; Lewinsohn, Rohde, & Seeley, 1994), most of the suicide attempts were in the mild to moderate range of lethality (Sapyta et al., 2012). The average medical lethality score of suicide attempts (unweighted for number of observations per participant) among individuals who made repeat attempts was 2.88 (SD = 1.92).
Assessment of life events
Life events before suicide attempts were assessed using all available information. Sources included (but were not limited to) a modified version of the Life Events Checklist (Johnson & McCutcheon, 1980), the symptom timelines that we developed in conjunction with the semistructured clinical interviews (ISCA and FISA; Sherrill & Kovacs, 2000), the queries regarding legal involvement of the Follow-Up Information Sheet, and precipitant section of the Suicide Circumstances Schedule (Brent et al., 1988). Negative life events in the 3 months before each suicide attempt were coded independently by at least two coders and discrepancies were resolved by consensus between the reviewers. If a participant explicitly described a life event as a precipitant, but was vague about the timing, we counted the life event as though it occurred within the 3-month period. In an interrater reliability trial, agreement between two independent coders regarding the presence/absence of a subset of major life events (loss and legal events) was 92.5% (κ = 0.85). For the events agreed upon by the two coders, there was 97.9% agreement as to the date (within a 2-week period of time). The total severity of life stress preceding suicide attempts was assessed in two different ways. First, we examined the unweighted total number of negative life events in the 3 months before suicide attempts. Second, the magnitude of social adjustment required by different life events (“life change units”) was estimated using the standardized table of life change unit values provided by Miller and Rahe (1997). The table of life change units provided by Miller and Rahe (1997) was derived in a scaling study, and represented a revision of the life change values originally described by Holmes and Rahe (1967). In previous studies, both the unweighted number of life events and life change units have been linked to poorer health outcomes (e.g., De Benedittis, Lorenzetti, & Pieri, 1990; Lantz, House, Mero, & Williams, 2005). The average number of life events and life change units in the 3 months before suicide attempts were 3.78 (SD = 2.61) and 184.55 (SD = 125.81), respectively.
Statistical Method
General approach and covariates
Given the number of observations over time and the multiple suicide attempts, we used longitudinal statistical models that can accommodate different numbers of observations per participant, varying amounts of time between observations, and missing data. The data were not analyzed as a panel study with specific “waves” of data and missing values when a scheduled assessment was delayed or missed. Rather, the data set was organized so that assessments for participants were consecutively numbered, regardless of when they occurred.
There were some missing data that the analyses could not accommodate. Specifically, there were five suicide attempts, all occurring before the index hospitalization, for which precise dates could not be estimated. These suicide attempts were not included in analyses of developmental trends, and of the intervals between consecutive suicide attempts. There were no missing life events or lethality data. There were 11 missing values (for 7.1% of suicide attempts at hospitalization or over the follow-up) regarding intent; in these cases, the participants reported enough information to indicate that there was at least some intent to die, but gave vague or inconsistent reports about the degree of intent or ambivalence. These data were viewed conservatively as missing at random (MAR) rather than being imputed, given that we did not know the mechanisms associated with the missing data (Little & Rubin, 1987; Rubin, 1987, 1996). The statistical models implemented in SAS were able to use full information available from the data, under the assumption of MAR.
Because gender (e.g., Lewinsohn et al., 2001) and race/ethnicity (see Goldston et al., 2008) have been found in previous studies to be related to suicide attempts, they were included as covariates in all analyses to reduce variance attributable to potentially confounding or background variables. Because age also has been noted to be related to the clinical characteristics of suicide attempts (Conwell et al., 1998; Hamdi, Amin, & Mattar, 1991; O’Brien et al., 1987), age was included as a time-varying covariate in models of intent and lethality to disentangle age effects from effects associated with increasing number of suicide attempts.
As preliminary analyses, we used linear regression to evaluate whether demographic variables (age at hospitalization, gender, or race/ethnicity), or number of suicide attempts at baseline hospitalization were related to either number of assessments completed, or the length of time in the study (log transformed to improve normality of distribution). We also used linear regression to examine whether variability in the timing of assessments (i.e., time between the assessment when a suicide attempt was reported, and the prior assessment) was related to four of the outcomes of the study (intent of suicide attempts, lethality of suicide attempts, number of life events before suicide attempts, or life change before suicide attempts). The timing of assessments was not examined in relation to the time between successive suicide attempts because many of the reported attempts occurred before initiation of the follow-up study.
Developmental model
A cubic polynomial logistic regression was used to examine suicide attempts as a function of age (z-transformed for numerical stability). This model was chosen over a linear or quadratic model because of the sharp rise in attempts in adolescence, followed by a tapering off in adulthood. This model was fitted in PROC GLIMMIX with variance-components covariance structure, which was assumed to be different for males and females. This model is equivalent to a generalized estimating equations (GEE) based approach except that GEE models do not have an option for a heterogeneous variance-covariance structure. As a conservative approach to modeling, we used sandwich (robust) variance estimates in the analyses, which provided additional protection against heterogeneity and departures from assumptions. Interactions with gender were explored, but eventually were not included in models because they were not reliably related to suicide attempts, and because of multicollinarity.
For descriptive purposes, the actual proportions of individuals with attempts as a function of age (in 2-year intervals) and gender were graphed in Figure 1. The curves were smoothed using the lowess function (Cleveland, 1981) in R (R Development Core Team, 2010). This Figure was not generated from the polynomial logistic regression model, but was based on aggregate data for clarity of presentation.
Figure 1. Proportion of sample with suicide attempts as a function of age. Curves were smoothed using a lowess function in R. The number of individuals for whom data at each age were available is as follows: ≤12 (n = 180), 13 (n = 178), 14 (n = 178), 15 (n = 174), 16 (n = 172), 17 (n = 171), 18 (n = 166), 19 (n = 162), 20 (n = 169), 21 (n = 159), 22 (n = 157), 23 (n = 156), 24 (n = 155), 25 (n = 151), 26 (n = 147), 27 (n = 143), 28 (n = 129), 29 (n = 106), 30 (n = 76), 31 (n = 55), 32 (n = 35), 33 (n = 20), and 34 (n = 6). Number of suicide attempts = 286.
Sensitization models
To examine whether intent and medical lethality of attempts increased as a function of number of prior suicide attempts, we used GEE implemented in SAS 9.2 (SAS Institute, Inc., 2008). This approach allowed us to account for the within-subject correlations from multiple observations. For both intent and lethality analyses, we used multivariate ordinal logistic models (with a cumulative logit link for the Likert scales).
To address the question of whether the amount of time between suicide attempts decreases as a function of number of suicide attempts, linear regression models using GEE were utilized. These models adjusted for within-subject correlations. The amount of time between successive suicide attempts was transformed to a logarithmic scale because of the nonnormal distribution of these times.
Generalized estimating equations were used to examine the relationship between the number of suicide attempts and life change in the 3 months before the most recent suicide attempt. The primary predictor in these analyses was the number of suicide attempts. In separate analyses, the total number of life events and life change scores in the 3 months before each suicide attempt were dependent variables. For the analyses regarding sum of life events as a dependent variable, we used negative binomial regression (with log link). For the analyses regarding life change scores as a dependent variable, we used a multiple linear regression.
In all analyses examining sensitization, robust (sandwich) variance-covariance estimates were used to adjust for heterogeneity and departures from assumptions. Results with model-based estimates of SEs were also examined and the pattern of results was nearly identical to results with robust estimates. Results with model-based estimates are not presented, but are available upon request.
Results Preliminary Analyses
Demographic variables and history of suicide attempts at baseline hospitalization were not related to the amount of time that participants were followed in this study (p values >.05). History of suicide attempts, age at hospitalization, and race/ethnicity were not related to the number of follow-up assessments (p values >0.05). However, females participated in more assessments than males (b = 1.546, SE = 0.763, t = 2.03, p = .044). The amount of time between a follow-up assessment when a suicide attempt was reported and the previous follow-up assessment was not related to intent or lethality of suicide attempts, number of life events before attempts, or life change before attempts (p values >0.05).
Developmental Trends in Suicide Attempts From Adolescence to Young Adulthood
As seen in Table 2, results from the cubic polynomial regression indicated that females made more suicide attempts than males and that the proportion of the sample with suicide attempts varied as a quadratic function of age. Specifically, as seen in the smoothed curve in Figure 1 using aggregate data, the rates of suicide attempts increased from early adolescence through mid-adolescence, peaked in mid-adolescence, and decreased again until the early 20s, whereupon the rates stabilized.
Age Patterns in Suicide Attempts (Cubic Polynomial Logistic Regression for Suicide Attempts as a Function of Age, z-Transformed)
The proportion of suicide attempts at each age that was made by individuals with prior attempts increased from ages 9–10 through adulthood. For example, between ages 9–10 and 15–16, the proportion of suicide attempts in any 2-year period that were repeated attempts ranged between 0.25 and 0.57. From ages 17–18 through 31–32, the proportion of attempts that were repeated attempts ranged from 0.67 to 0.90.
Suicide Intent and Medical Lethality as a Function of Number of Attempts and Age
Suicide intent was positively related both to number of prior suicide attempts and to increasing age in separate models. In contrast, medical lethality of suicide attempts increased with participants’ age (see Table 3), but was not related to number of suicide attempts. Gender and race/ethnicity were not related to intent or lethality.
Tests of the Sensitization Model for Suicide Attempts
Intersuicide Attempt Intervals as a Function of Number of Attempts
As reflected in Table 3, the log-transformed intersuicide attempt intervals decreased in length as a function of the number of past suicide attempts. Converting to the original duration scale by exponentiation, the associated “hazard ratio” was 0.722 per unit increase in the number of past attempts. That is, the more suicide attempts an individual made, the shorter the period of time before the next repeat suicide attempt on average. Neither gender nor race/ethnicity was related to the amount of time between repeat suicide attempts.
Life Events and Suicide Attempts
As seen in Table 3, neither total number of life events nor the sum of life change scores from the 3 months preceding suicide attempts was related to the number of suicide attempts an individual had made.
DiscussionIn this prospective, naturalistic study, we examined patterns in recurrent suicidal behavior among adolescents and young adults, and the degree to which these patterns were consistent with a sensitization model. In this high-risk sample, the rate of suicide attempts among both males and females increased through mid-adolescence, and then decreased during young adulthood, stabilizing by the mid-20s. These developmental patterns are similar to those noted in epidemiological research (Boeninger et al., 2010; Joffe et al., 1988; Kessler et al., 1999; Lewinsohn et al., 2001; Velez & Cohen, 1988; Wunderlich et al., 2001). However, in an extension of these previous studies, we found that by the transition to adulthood, the majority of suicide attempts were made by individuals who already had a history of attempts. Future studies are needed to establish whether a similar pattern would be observed in larger-scale epidemiologic samples.
Given the high rate of repeat suicidal behavior in this sample, it is critical to examine whether suicidal behavior escalates with recurrences, and whether sensitization processes might account for any escalation. As predicted from a sensitization model, the intent of suicide attempts did increase as individuals made a greater number of attempts. The intent of suicide attempts also increased as participants got older, and the effects of increasing age and the number of prior suicide attempts were confounded to a degree. Previous findings regarding the clinical characteristics of earlier versus subsequent suicide attempts (or first-time as contrasted with repeat attempts) have yielded contradictory findings (Forman et al., 2004; Kaslow et al., 2006; Michaelis et al., 2003; Ojehagen et al., 1992; Reynolds & Eaton, 1986). However, these studies generally have been cross-sectional and focused on suicide attempts that precipitated treatment entry, which may not be a “representative” period of time in the natural history of suicidal behavior. The finding that intent increases with number of attempts contradicts the common clinical myth that individuals who make multiple attempts “are not serious” about killing themselves. To the contrary, these individuals seem to become more determined and have less ambivalence about dying with successive attempts.
Another index of severity, the medical lethality of suicide attempts, increased as a function of age, but was not related to the cumulative number of attempts. This finding was not consistent with what would have been predicted by a sensitization model. On the other hand, the finding of increased lethality with increasing age dovetails with other findings that lethality of suicide attempts in some adult patient populations is positively correlated with age (Shearer et al., 1988), and that individuals at older ages are more likely than individuals at younger ages to die by suicide when they engage in suicidal acts (Friedmann & Kohn, 2008). Sapyta et al. (2012) found that intent and lethality are not strongly correlated among adolescents and young adults, although both maximum intent and maximum lethality of past attempts were predictive of future suicidal behavior. This finding could be due in part to restricted access to more lethal methods at younger ages, the fact that adolescents feel constrained in choice of methods because they live with parents, the lack of knowledge about the medical consequences associated with different methods at younger ages (Brown, Henriques, Sosdjan, & Beck, 2004), or greater planning and premeditation among older individuals (Conwell et al., 1998).
The second prediction from a sensitization model was that there would be decreasing intervals of time between successive suicide attempts. Although the amount of time between suicide attempts was quite variable, overall, there was a decreasing length of time between suicide attempts as the number of suicide attempts increased. This possibility, to our knowledge, has not been evaluated previously. The prospective, repeated assessments design of the current study made it particularly well suited for examining the length of time between attempts. The pattern of decreasing amounts of time between successive attempts highlights the possibility of increasing vulnerability associated with repeated occurrences of suicidal behavior.
Last, the sensitization model is predicated on the notion that individuals become more reactive or sensitive to stress through repeated exposure. In this study, life stress measured in two different ways was unrelated to the number of prior suicide attempts. Although these results are consistent with several findings from cross-sectional studies with adults (Crane et al., 2007; Joiner & Rudd, 2000; Kaslow et al., 2006), it should be noted that the sample size for the life stress analyses (n = 36 with 129 suicide attempts) was smaller than for the other samples used for tests of sensitization hypotheses. Therefore, although there was very little indication of an effect, it could be the case that the sample size was not sufficiently large to detect patterns in reactivity to life stress across individuals. In addition, it is possible that approaches or measures for assessing the relationship between life stress and suicidal behavior to date have not been sufficiently sensitive for detecting patterns or reactivity to stress. It also is possible that the heterogeneity among individuals who attempt suicide is so great that any evidence of a sensitization process is obscured in-group analyses. For example, some suicidal individuals may become more reactive to life stress, whereas others, rather than being more reactive, simply experience an inordinate number of negative life stresses, some of which could even be related to their own mental health difficulties (Conway, Hammen, & Brennan, 2012). Another possibility is that some individuals become especially reactive to only certain types of stresses (e.g., losses or difficulties in relationships), and the sensitization process is not apparent across the whole spectrum of major life stresses. Joiner (2005), for instance, has emphasized life circumstances associated with thwarted belongingness and perceived burdensomeness, and Shneidman (1998) emphasized the importance of circumstances associated with unmet psychological needs in the etiology of suicide.
In summary, the results from this longitudinal study revealed a pattern of escalation of suicidal behavior, with increasing intent and decreasing amounts of time between successive attempts. The data were inconclusive as to whether a sensitization model might account for this escalation. It is possible that repeated exposures to stresses or situations that provoked earlier suicidal behavior change the way individuals think or respond affectively to future situations, and additional research is needed to examine this possibility. Nonetheless, the prospective findings of this study importantly underscore the observation that there is an escalation in suicidal behavior that occurs as individuals make a greater number of attempts. If not because of sensitization, there are multiple possibilities for this escalation of suicidal behavior. For example, increasing suicidal behavior may be reflective of increasing distress with persisting difficulties, or of increasing severity of psychopathology. In addition, Linehan (1993) suggested that individuals who have vulnerabilities with emotion regulation may respond to invalidating and inconsistent responses from others with escalating self-destructive behavior. It also is possible that there is other “scarring” that occurs with prior suicide attempts, which renders individuals more vulnerable for future episodes of suicidal behavior. Kessing, Hansen, Andersen, and Angst (2004) have demonstrated that scarring or episode sensitization is one mechanism that may account for recurrent episodes of affective disorder.
The findings from this study have multiple implications for mental health professionals. In working with suicidal clients, clinicians need to be aware that the intent associated with suicidal behavior may increase with repeated attempts, and that both intent and lethality of suicidal behavior may increase as individuals get older. Clinicians sometimes consider recurrent suicidal behavior with less urgency than they should, mistakenly assuming that individuals who make multiple attempts may not be serious about killing themselves. In fact, the opposite appears to be true. As individuals make repeated attempts, they are on average more intent on dying by suicide.
The finding, that for some individuals there is an escalation of suicidal behavior after an initial attempt, underscores the importance of developing effective prevention programs for at-risk populations before individuals have made their first attempt, and potentially entered into this pattern of escalation. For individuals who already have made attempts, there is a strong need for relapse prevention interventions that can interrupt the cycle of recurrent suicidal behavior before there is further escalation. The use of chain analysis and the focus on development of coping skills in dialectical behavior therapy (Linehan, 1993) and the complementary use of the relapse prevention task in cognitive behavior therapy for suicide prevention (Brown et al., 2005) are two promising approaches to relapse prevention. Nonetheless, suicidal individuals often terminate treatment prematurely or fail to initiate treatment after referrals (Dahlsgaard, Beck, & Brown, 1998; Rudd, Joiner, & Rajab, 1995). When individuals terminate treatment prematurely, they may not learn the skills or alternatives to suicidal behavior they need for forestalling future episodes and interrupting this cycle of escalation. Therefore, it is important that effective approaches (e.g., drawing from motivational enhancement strategies) be developed for facilitating treatment engagement and follow-through so that suicidal individuals maximally benefit from relapse prevention activities (Britton, Patrick, Wenzel, & Williams, 2011).
Several caveats regarding the findings from this study should be acknowledged. First, the sample was recruited from an inpatient adolescent psychiatric unit. The findings from the study may not be generalizable to other populations, including individuals who have not been hospitalized or older populations. Second, there was variability both within and between participants in the intent and lethality of suicide attempts, the number and type of stressful life events preceding suicide attempts, and the length of intersuicide attempt intervals. As such, the effects of sensitization may not always be detectable or apparent on an individual basis. Third, although we had a large number of observations over a relatively long period, which contributed to the power of our analyses and provided opportunities to see unfolding patterns, the actual number of individuals on which some results are based is still smaller than would be ideal. In particular, the analyses regarding life stress focused on only 36 individuals who made 129 attempts over the follow-up. Hence, it will be important for these findings to be replicated. Fourth, no one in this study had died by suicide so the degree to which these processes are applicable to suicide deaths is not clear. Fifth, although severity of life stress preceding suicide attempts was measured in two different ways, using all available information, we did not use specific interviews for assessing life events, which would have provided more contextual information about the life stresses. Sixth, this study did not examine potential psychiatric factors including treatment history, increasing severity of psychiatric and substance use problems, or exposure to childhood adversity that could have shed light on the mechanisms associated with escalation or sensitization. These caveats notwithstanding, the pattern of results from this prospective study provided evidence of escalating suicidal behavior among individuals who make repeat suicide attempts in adolescence and young adulthood, even if the tests of a sensitization model per se were not fully supported. A better understanding of the processes underlying this escalation will be important to inform the design of more effective relapse prevention interventions and intervene in patterns that culminate in repeat suicidal behaviors.
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Submitted: February 6, 2013 Revised: October 31, 2014 Accepted: November 12, 2014
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Source: Journal of Consulting and Clinical Psychology. Vol. 83. (2), Apr, 2015 pp. 253-264)
Accession Number: 2015-02672-001
Digital Object Identifier: 10.1037/a0038657
Record: 48- Title:
- Suicide risk in older adults: Evaluating models of risk and predicting excess zeros in a primary care sample.
- Authors:
- Cukrowicz, Kelly C.. Department of Psychology, Texas Tech University, Lubbock, TX, US, kelly.cukrowicz@ttu.edu
Jahn, Danielle R., ORCID 0000-0003-0156-9680. Department of Psychology, Texas Tech University, Lubbock, TX, US
Graham, Ryan D.. Department of Psychology, Texas Tech University, Lubbock, TX, US
Poindexter, Erin K.. Department of Psychology, Texas Tech University, Lubbock, TX, US
Williams, Ryan B., ORCID 0000-0001-5718-753X. Department of Agriculture & Applied Economics, Texas Tech University, Lubbock, TX, US - Address:
- Cukrowicz, Kelly C., Department of Psychology, Texas Tech University, Mail Stop 42051, Lubbock, TX, US, 79409-2051, kelly.cukrowicz@ttu.edu
- Source:
- Journal of Abnormal Psychology, Vol 122(4), Nov, 2013. pp. 1021-1030.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 10
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- interpersonal theory, older adults, suicide, zero-inflated negative binomial model, statistical approaches, death ideation, suicide ideation
- Abstract:
- Research is needed that examines theory-based risk factors for suicide in older adults. The interpersonal theory of suicide (Joiner, 2005; Van Orden et al., 2010) provides specific hypotheses regarding variables that contribute to the development and variability in death ideation and suicide ideation; however, data suggest that older adults may not report suicide ideation in research settings or to treatment providers even when they experience it (Heisel et al., 2006). The purpose of this study was to test theory-based predictions regarding variables that contribute to death ideation (i.e., a passive wish to die) and suicide ideation in older adults. This study introduces the application of zero-inflated negative binomial regression (ZINB) to the study of suicidal behavior. ZINB was used to test theory-based predictions, while also testing a hypothesis regarding variables associated with denial of suicide ideation among participants who endorsed risk factors associated with suicide risk. Participants included 239 adults aged 60 and older recruited from primary care clinics who completed a variety of self-report instruments. The results of this study indicated that perceived burdensomeness and hopelessness were significantly associated with variability in death ideation. Additional results indicated that elevated scores on thwarted belonging, the interaction between perceived burdensomeness and hopelessness, and the interaction between thwarted belonging and perceived burdensomeness were associated with a significant reduction in the probability of a participant being a suicide ideator. These results offer substantial support for the interpersonal theory of suicide. The implications of these findings are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Mathematical Modeling; *Statistical Analysis; *Suicidal Ideation; *Suicide; *Theories; Aging; Risk Factors
- Medical Subject Headings (MeSH):
- Aged; Attitude to Death; Binomial Distribution; Depressive Disorder; Female; Geriatric Psychiatry; Humans; Interpersonal Relations; Male; Middle Aged; Models, Psychological; Primary Health Care; Regression Analysis; Risk Factors; Suicidal Ideation; Suicide; Surveys and Questionnaires
- PsycINFO Classification:
- Behavior Disorders & Antisocial Behavior (3230)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Mini Mental Status Exam
Geriatric Suicide Ideation Scale
Interpersonal Needs Questionnaire DOI: 10.1037/t10483-000
Beck Hopelessness Scale
Center for Epidemiologic Studies Depression Scale
Center for Epidemiological Studies Depression Scale DOI: 10.1037/t02942-000 - Grant Sponsorship:
- Sponsor: American Foundation for Suicide Prevention, US
Recipients: No recipient indicated - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Oct 7, 2013; Revised: Sep 3, 2013; First Submitted: Nov 16, 2012
- Release Date:
- 20131223
- Correction Date:
- 20150216
- Copyright:
- American Psychological Association. 2013
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0034953
- PMID:
- 24364604
- Accession Number:
- 2013-44247-008
- Number of Citations in Source:
- 53
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-44247-008&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-44247-008&site=ehost-live">Suicide risk in older adults: Evaluating models of risk and predicting excess zeros in a primary care sample.</A>
- Database:
- PsycINFO
Suicide Risk in Older Adults: Evaluating Models of Risk and Predicting Excess Zeros in a Primary Care Sample
By: Kelly C. Cukrowicz
Department of Psychology, Texas Tech University;
Danielle R. Jahn
Department of Psychology, Texas Tech University
Ryan D. Graham
Department of Psychology, Texas Tech University
Erin K. Poindexter
Department of Psychology, Texas Tech University
Ryan B. Williams
Department of Agricultural & Applied Economics, Texas Tech University
Acknowledgement: Funding for this study was provided by the American Foundation for Suicide Prevention. We would also like to thank the following individuals for their contributions to these studies: M. David Rudd, Yeates Conwell, Phillip Smith, Erin Schlegel, Matt Jacobs, Brandy Ledbetter, Sean Mitchell, Justin Stevens, Amy Bryant, Kim Allen, Samantha Strople, Cara Cates, Laura Nelson, Shannon Bracket, and Evan Guidry.
Compared with those in other age groups, older adults are at significant risk for death by suicide (CDC, 2012). The rate of death by suicide increases steadily from age 65 to 85, with the highest rate of suicide deaths among older adults ages 85 and older (CDC, 2012). Furthermore, the ratio of suicide attempts to deaths by suicide is in the range of 25:1 for all ages combined, whereas for adults over age 65 it is 4:1 (CDC, 2012), highlighting the pronounced risk for death by suicide in late life compared with younger cohorts. Paradoxically, previous research has also indicated that the rates of reported suicide ideation and suicide attempts decreases with increasing age in older adults (Duberstein et al., 1999; Lynch et al., 1999). As such, Witte et al. (2006) suggest that any endorsement of suicide ideation should be a strong indicator of risk for suicide attempt and death by suicide in older adults. Given the low attempt to death ratio for older adults (i.e., 4:1), studies including primarily suicide attempters may not allow us to understand the multifaceted nature of suicide risk in older adults who die on a first or early suicide attempt. Therefore, it is critical that studies include community samples of older adults who represent a broad range of suicide risk.
Currently, our knowledge regarding older adult suicide risk is limited in two fundamental ways. First, more research is needed that examines theory-based risk factors for suicide in older adults using falsifiable predictions. Second, earlier research has suggested that older adults may not report suicide ideation in research settings or to treatment providers (e.g., mental health providers, primary care physicians), even when they experience it (Heisel et al., 2006). To date, the vast majority of studies have used continuous scales of suicide risk, assuming accuracy in reporting across the continuum. However, given that some individuals with low scores may be underreporting suicide ideation, this approach may be inadequate. Because older adults may still endorse other risk factors associated with suicide ideation (e.g., depressive symptoms, hopelessness), it may be informative to assess risk using an expanded set of indicators related to theory-based risk factors, allowing prediction of those who are at risk for ideation and those who are not. The current study addressed these limitations through the use of theory-based hypotheses regarding the prediction of death ideation and suicide ideation in older adults, and the use of a novel statistical approach for examining suicide risk among older adults.
Reporting of Suicide Ideation Among Older AdultsA critical consideration for any study examining suicide ideation in older adults is the potential for reporting bias, as noted above. This suggests a need for research that uses novel statistical procedures to identify two types of older adults: nonideators (i.e., individuals who deny suicide ideation and who report nonexistent risk on other variables associated with suicide ideation), and potential ideators (i.e., those who deny suicide ideation while simultaneously reporting other established risk factors for suicide).
To adequately examine variables that may be associated with nonideator or potential ideator status, community samples of older adults are necessary to ensure sufficient representation of the range of suicide risk in the population. The sampling of community participants, however, frequently results in a large percentage of respondents with zero (or equivalent) scores on the outcome measure (e.g., death ideation, suicide ideation). Common approaches to analyzing this type of data include multiple linear regression, Poisson regression, and negative binomial regression, but the use of these approaches results in misspecification, such that the regression coefficients for the predictor variables are unstable (cf., Gurmu & Trivedi, 1996). Binary logistic regression can be used with samples including a large percentage of zeros; however, this approach only predicts the presence or absence of an experience (e.g., suicide ideation vs. no suicide ideation), without explaining differences in severity when risk factors are present.
One approach that is used more frequently in other fields (e.g., economics, biometrics, health-care research, ecological studies; Gurmu & Elder, 2008; Hur, Hedeker, Henderson, Khuri, & Daley, 2002; Lambert, 1992; Minami, Lennert-Cody, Gao, & Román-Verdesoto, 2007) is zero-inflated modeling, for instance, zero-inflated Poisson regression and zero-inflated negative binomial regression (ZINB). Recent publications have also described the application of these approaches to psychological sciences as well (Atkins & Gallop, 2007; Coxe, West, & Aiken, 2009). As summarized by Minami and colleagues (2007), zero-inflated models accomplish two objectives regarding the prediction of the outcome variable in the presence of excess zeros. These models simultaneously estimate a binary logistic regression and a negative binomial or Poisson regression, while also accounting for the existence of two unique types of zeros. In relation to the current study, one type of zero (i.e., excess zeros) occurs in participants who deny suicide ideation and have little or no psychological distress (i.e., nonideators). It is important to note, given the current responses to questions pertaining to suicide-risk factors, individuals with this type of zero are highly unlikely to convert to nonzero on suicide ideation. The other type arises from participants who deny suicide ideation while reporting other empirically based risk factors (e.g., depression, hopelessness) for suicide ideation (i.e., potential ideators). The binary logistic portion of the model provides estimates of the likelihood of the dichotomous outcome (i.e., whether a participant is a nonideator or potential ideator). The negative binomial regression provides an estimate of the continuous relationship between the predictor variables and the outcome measure (i.e., death ideation, suicide ideation), having controlled for the effect of nonideators (i.e., excess zeros) on the estimation of ideation.
To date, no studies have used ZINB to identify variables associated with denial of suicide ideation among older adults. This approach allows for differentiation between zero-ideation responses reflecting the absence of psychological distress associated with suicide risk, and zero-ideation responses occurring in participants who report distress variables that are correlated with suicide ideation. Therefore, we used this statistical approach, in combination with a theory-based model of risk factors (detailed below), to predict denial of suicide ideation.
Risk Factors for Late-Life Suicide: Suicide Ideation, Death Ideation, Depression, and Hopelessness
Prior studies have suggested that suicide ideation is a significant risk factor for suicide deaths in older adults (Conwell, Duberstein, & Caine, 2002; Conwell, Van Orden, & Caine, 2011). Researchers have also found increased risk for suicide in older adults reporting death ideation, symptoms of depression, and hopelessness (e.g., Baca-Garcia et al., 2011; Conwell et al., 1996; Cukrowicz et al., 2009; Rao, Dening, Brayne, & Huppert, 1997; Suokas, Suominen, Isometsa, Ostamo, & Lonnqvist, 2001). Although death ideation has historically been considered a less severe indictor of risk for suicide, several studies have concluded that adults reporting death ideation are similar to those reporting suicide ideation (Baca-Garcia et al., 2011; Rao et al., 1997; Suokas et al., 2001). For example, Baca-Garcia and colleagues (2011) examined death ideation and suicide ideation as predictors of suicide attempt using data from two large nationally representative surveys. The results indicated that the risk for lifetime suicide attempt was not significantly different for those with a history of death ideation, compared with those with a history of suicide ideation. This is consistent with other empirical findings examining outcomes for older adults reporting death ideation (Rao et al., 1997; Suokas et al., 2001), suggesting that researchers should assess for death ideation in studies examining risk for suicide among older adults.
A significant body of literature has also found that depression and hopelessness are associated with suicide risk among older adults (Conwell et al., 1996; Conwell et al., 2002; Cukrowicz et al., 2009; Cukrowicz, Cheavens, Van Orden, Ragain, & Cook, 2011; Scocco, Meneghel, Dello Buono, & De Leo, 2001; Szanto et al., 2007; Turvey et al., 2002). These studies indicate that major depressive disorder is the most common psychiatric disorder in older adults who have died by suicide (Conwell et al., 1996; Conwell et al., 2002). A large 10-year prospective study of predictors for late-life suicide found that depressive symptoms, perceived health status, medical status, cognitive difficulties, and affective functioning predicted suicide deaths (Turvey et al., 2002). Further, several studies have indicated significant associations between depressive symptoms and suicide ideation in community samples of older adults, as well as reduction in suicide ideation following reduction in depressive symptoms for depressed older adults participating in treatment (Cukrowicz et al., 2009; Cukrowicz et al., 2011; Scocco et al., 2001; Szanto et al., 2007; Vannoy et al., 2007). Likewise, numerous studies have indicated that hopelessness plays a significant role in suicide ideation and suicide deaths among older adults (Britton et al., 2008; Szanto, Reynolds, Conwell, Begley, & Houck, 1998). These studies indicate that hopelessness is associated with the presence and severity of suicide ideation (Britton et al., 2008). In addition, hopelessness may remain high in older adult suicide attempters (compared with suicide ideators without attempt histories and nonsuicidal older adults) even following medication treatment for depression (Szanto et al., 1998). Taken together, this research shows that death ideation, depressive symptoms, and hopelessness should all be included in studies examining risk for suicide among older adults.
The interpersonal theory of suicide (Joiner, 2005; Van Orden et al., 2010) has generated additional variables (i.e., thwarted belonging and perceived burdensomeness) that may contribute significantly to suicide or death ideation in older adults. Thwarted belonging is conceptualized as an absence of social relationships that results in feeling disconnected or without a sense of belonging (Joiner, 2005). Furthermore, thwarted belonging may develop when an individual lacks reciprocal caring relationships (i.e., lacking support in times of need or believing that he or she does not provide support to others; Van Orden et al., 2010). The second variable proposed by the interpersonal theory of suicide, perceived burdensomeness, is the perception that a person is a liability to others, which generates feelings of self-hatred (Van Orden et al., 2010). A person may feel like he or she is a burden on family members due to mental illness or physical disability, or due to the perception that he or she is not contributing to others because of unemployment or other limitations (Van Orden et al., 2010). It is important to note, empirical data examining older adult suicide risk supports significant associations between perceived burdensomeness and suicide ideation (Cukrowicz et al., 2011; Jahn, Cukrowicz, Linton, & Prabhu, 2011; Marty, Segal, Coolidge, & Klebe, 2012), as well as between thwarted belonging and suicide ideation (Marty et al., 2012; McLaren, Gomez, Bailey, & van der Horst, 2007).
The Interpersonal Theory Provides Testable Models of Suicide Risk in Older Adults
Van Orden and colleagues (2010) made two predictions regarding the associations between interpersonal theory variables and death or suicide ideation. The first prediction specifically suggests that individuals who feel a lack of connection to others, or perceive themselves as a burden on others, may wish for death as a way to reduce these aversive states (Van Orden et al., 2010). For example, an individual who feels disconnected from others, and that others do not care about him or her, may feel that it would be easier to disappear or not wake up, rather than continue feeling a thwarted sense of belonging. In relation, individuals who perceive that their lives detract from the well-being of others may feel that others would be better off if they were dead. As such, Van Orden et al. (2010) predicted that the presence of either thwarted belonging or perceived burdensomeness would be associated with death ideation. The second prediction suggests that the simultaneous presence of thwarted belonging and perceived burdensomeness would be associated with suicide ideation (e.g., “I want to kill myself”), but only when an individual feels hopeless that these states will change (Van Orden et al., 2010).
While the empirical data outlined above has examined the interpersonal theory’s constructs as correlates of suicide ideation, no research has yet examined these specific predictions in a multifaceted model of suicide risk in older adults. The present study tested these predictions in a model that also included depressive symptoms and hopelessness. The inclusion of empirically based risk factors for death ideation and suicide ideation (i.e., depressive symptoms, hopelessness, thwarted belonging, and perceived burdensomeness) allowed us to better account for the unique predictions proposed by the interpersonal theory (Van Orden et al., 2010).
Thus, our first hypothesis predicted that perceived burdensomeness, thwarted belonging, depressive symptoms, and hopelessness would each be significantly associated with death ideation. We further hypothesized that perceived burdensomeness and thwarted belonging would account for greater unique variance in death ideation than other included predictors. Our second hypothesis predicted the presence of a three-way interaction between thwarted belonging, perceived burdensomeness, and hopelessness, such that those with thwarted belonging and perceived burdensomeness would report the greatest suicide ideation if they also reported elevated hopelessness. Finally, we hypothesized a significant three-way interaction, such that elevated scores on thwarted belonging, perceived burdensomeness, and hopelessness would be associated with significantly reduced probability of being a nonideator.
Method Participants
Participants were 239 adults ages 60 and older (M = 72.4, SD = 6.9) recruited from a primary care setting at a southwestern university health-sciences center (cf. Cukrowicz et al., 2011; Jahn, Poindexter, Graham, & Cukrowicz, 2012; Van Orden, Cukrowicz, Witte, & Joiner, 2012). To identify potentially eligible participants, research personnel reviewed upcoming physician appointments for individuals aged 60 years and older from two primary care settings. Patients who met inclusion criteria (i.e., no physician note of bipolar disorder or mania, psychotic disorder, severe memory impairment, or cognitive difficulties, and had not previously participated or declined participation) were identified as potential participants. In order to maximize variability on study variables, this study did not select participants based on an elevated level of suicide ideation or death ideation, nor on a history of suicide attempt or self-injury. Potential participants were either mailed letters describing the study and subsequently contacted to determine their interest in the study or were approached at a scheduled medical appointment. A total of 436 letters were sent, with 105 patients agreeing to participate; 675 additional participants were approached at physician appointments, with 167 agreeing to participate. Participants came to the first author’s research clinic for study participation or, if they were unable to travel to the clinic, research assistants conducted study sessions at participants’ homes. Following consent, participants completed the Mini Mental Status Exam (MMSE; Folstein, Folstein, & McHugh, 1975), with a required minimum score of 25 for participation. Twenty-three participants were excluded due to MMSE scores and were provided referral information, four participants were missing a significant amount of data for the variables of interest in this study, and six participants with influential data points were dropped (see below).
The final sample consisted of 144 women (60.3%) and 95 men (39.7%). Marital status for this sample was: 66.7% married, 18.4% widowed, 7.9% divorced, 4.2% living with partner, 2.0% never married, 0.8% separated from spouse, and 0.8% in an intimate relationship but not living with partner. Participants were 90.8% Caucasian, 6.3% Hispanic, 1.7% African American, and 1.2% other. The mean total years of education for this sample was 14.4 (SD = 3.5). Fifty-eight participants (24.3%) reported a previous diagnosis of a psychological disorder. At the time of participation, 28 participants (11.7%) had a score greater than or equal to 16 on the Center for Epidemiological Studies Depression Scale (CES-D; Radloff, 1977), suggesting significant symptoms of depression. Within this sample, 12 participants (5.0%) had scores on the death-ideation and suicide-ideation variables greater than or equal to one standard deviation above the mean; 16 participants (6.7%) had scores indicating death ideation, but not suicide ideation, greater than or equal to one standard deviation above the mean, and 19 participants (7.9%) had suicide-ideation, but not death-ideation, scores greater than or equal to one standard deviation above the mean. Seven participants (2.9%) reported a history of suicide attempt. Sixty-four participants (26.8%) reported a lifetime diagnosis of a mental or psychological disorder. Diagnoses included depression (n = 44; 18.4%), anxiety disorder (n = 8; 3.3%), substance-use disorder (n = 4; 1.7%), bipolar disorder (n = 3; 1.3%), cognitive disorder (n = 1; 0.4%), schizophrenia (n = 2; 0.8%), and other or unknown (n = 2; 0.8%). Twenty-three participants (9.6%) indicated that they had been diagnosed with a mental or psychological disorder within the past 12 months. Diagnoses included depression (n = 16; 6.7%), anxiety disorder (n = 2; 0.8%), bipolar disorder (n = 3; 1.3%), substance-use disorder (n = 1; 0.4%), and other (n = 1; 0.4%).
Procedure
All procedures for this study were in accordance with protocol approved by the university institutional review board. Older adults electing to take part in the study provided informed consent and completed self-report questionnaires as well as semistructured clinical interviews. Given that some questions pertained to current and past suicidal behaviors, all research personnel were trained in suicide-risk assessment. Researchers examined all participant responses related to suicide risk. In the event that a participant was identified as at risk, follow-up procedures and interventions were performed in accordance with the approved protocol. When participants completed the study, they were given a referral sheet that provided local mental health resources and were compensated for their time.
Measures
Beck Hopelessness Scale (BHS)
The BHS (Beck & Steer, 1988) is a 20-item true/false self-report questionnaire assessing negative cognitions and emotions about the future (Beck & Steer, 1988). Each item (e.g., “I might as well give up because I can’t make things better for myself”) is scored as either a 0 or 1; a sum total is computed (range 0 to 20), with higher scores reflecting greater hopelessness (Beck & Steer, 1988). Previous research has supported the reliability of this scale across a variety of populations (Glanz, Hass, & Sweeney, 1995). The BHS internal consistency in the current sample was good (Cronbach’s α = .85).
Center for Epidemiologic Studies Depression Scale
The CES-D (Radloff, 1977) is a 20-item self-report questionnaire assessing severity of depressive symptoms. Participants were asked to indicate the frequency of items (e.g., “I thought my life had been a failure”) based on the last 7 days using a Likert scale ranging from 0 (rarely or none of the time) to 3 (most or all of the time). Increasing scores indicate more severe depressive symptoms. Research has supported the psychometric properties of this scale when used with older adults (Beekman et al., 1997; Lewinsohn, Seeley, Roberts, & Allen, 1997). Internal consistency in this sample was good (Cronbach’s α = .89).
Geriatric Suicide Ideation Scale (GSIS)
The GSIS (Heisel & Flett, 2006) is a 31-item self-report measure of suicide ideation designed specifically for use in older adults. It is comprised of four subscales: suicide ideation, death ideation, loss of personal and social worth, and perceived meaning in life (Heisel & Flett, 2006). For the purposes of the current study, only the suicide-ideation (10 items) and death-ideation (five items) subscales were utilized in analyses. In this study, one item was removed from the suicide-ideation subscale (i.e., “I frequently think that my family will be better off when I am dead”) to reduce multicollinearity because it was related to perceived burdensomeness, resulting in a 9-item suicide-ideation subscale. Participants rated each statement (e.g., “I have seriously considered suicide more than once earlier in my life,” “I welcome the thought of drifting off to sleep and never waking up”), using a five point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). For the suicide-ideation and death-ideation subscales, total scores range from 9 to 45 and 5 to 25, respectively, with higher scores indicting greater ideation and therefore greater risk. Analyses for this study included a suicide-ideation or death-ideation score with a low-end zero score. For suicide ideation, 9 points were subtracted from all scores; for death ideation, 5 points were subtracted from all scores. Studies with older adults have shown that these subscales have adequate internal consistency reliability (Heisel & Flett, 2006; Marty, Segal, & Coolidge, 2010). Internal consistency in the current sample was adequate for the suicide-ideation subscale (Cronbach’s α= .81), as well as for the death-ideation subscale (Cronbach’s α = .67).
Interpersonal Needs Questionnaire (INQ)
The INQ (Van Orden et al., 2012) is a 15-item self-report questionnaire with two subscales, which measure thwarted belonging and perceived burdensomeness. The thwarted-belonging subscale consists of nine items that assess the degree of belonging that an individual experiences (e.g., “These days I am close to other people,” which is reverse scored). The six-item perceived-burdensomeness subscale measures the extent to which one feels like a burden on others and perceives that his or her death is more valuable than his or her life (e.g., “These days I think I am a burden on society”). Participants rated each statement using a 7-point Likert scale ranging from 1 (not at all true for me) to 7 (very true for me; Van Orden et al., 2012). For each subscale, responses are then totaled such that higher scores indicate greater thwarted belonging and perceived burdensomeness. In this sample, Cronbach’s alpha for the thwarted-belonging subscale was .84. For the perceived-burdensomeness subscale, Cronbach’s alpha was .74.
Data Analysis
We assessed for influential data points using recommendations for Cook’s D (Cook & Weisberg, 1982), Mahalanobis distance (Stevens, 1984), and centered leverage (Chatterjee & Hadi, 1988). Six participants’ data exceeded all three cutoff values. Inspection of the death-ideation and suicide-ideation outcome-variable histograms showed relatively large numbers of zero-value responses as a proportion of the total sample size (death ideation = 66/239; suicide ideation = 104/239). The data from samples in suicide research often contain a relatively large number of zero or near-zero values, making traditional ordinary least-squares regression analysis inappropriate. There has been a growing trend in this literature to employ Poisson regression (e.g., Casey, Gemmell, Hiroeh, & Fulwood, 2012; Chan, Chiu, Lam, Leung, & Conwell, 2006; Fang et al., 2012; Kleiman, Miller, & Riskind, 2012); however, this approach fails to account for the presence of excess zero observations, which are common in community samples in which a large portion of the sample has low suicide risk. The existence of excess zeros may be the result of overdispersion (i.e., when the conditional variance exceeds the mean) or nonlinearities in responses (Gurmu & Trivedi, 1996). In the case of overdispersion, the use of an overdispersed Poisson regression, particularly negative binomial regression, is common (Cameron & Trivedi, 1998; Elhai, Calhoun, & Ford, 2008; Long, 1997); however, the use of an overdispersed Poisson regression does not account for the observed zeros that are the result of nonlinearities in responses (i.e., a large number of zero responses), and its use would lead to inconsistent parameter estimates.
One solution to this problem is the use of a two-part count model or hurdle model. These models treat the zero values of the outcome variable differently than the positive values by assuming that the zeros are the result of a different data-generating process than the nonzeros. For example, it is solely up to the individual whether or not to pursue treatment for a psychological disorder (zero counts vs. positive counts), because the number of treatment sessions is determined by both the patient and the therapist (the magnitude of positive count). These models require two distinct estimations: one for the zero-generating process (logit or probit) and one for the variability in positive counts (typically either Poisson or negative binomial regression). Although this approach is attractive for dealing with excess zeros in the data, the treatment of all zeros as arising from the same process may be inappropriate for our sample. An alternative modified count model capable of addressing this concern is the zero-inflated model (Greene, 2012), which is a more acceptable approach, given our assumptions about the data-generating process for our sample.
The zero-inflated modeling approach allows for simultaneous estimation of both the zero and positive responses in the data. This process assumes that some of the zeros are part of the natural distribution of zero responses (i.e., nonideators), whereas there are additional zeros that are explained by a different process than that yielding the distribution of positive responses (i.e., potential ideators; Atkins & Gallop, 2007). Due to the combination of overdispersion and excess zeros in the data, we employed ZINB regression. In addition, given the potential for bias in the binary logistic regression in the presence of heteroscedasticity, we used robust heteroscedastic standard errors. STATA/MP 12.0 (StataCorp, College Station, TX) was used to estimate the models. The predictor variables for death ideation were perceived burdensomeness, hopelessness, depression, and thwarted belonging. The same predictor variables were included in the model for suicide ideation; however, two-way and three-way interactions between perceived burdensomeness, hopelessness, and thwarted belonging were added to maintain consistency with the theorized relationships.
A statistical test was employed to verify the appropriateness of the ZINB regression for this data (Atkins & Gallop, 2007). Vuong’s test (Vuong, 1989) was used to evaluate the existence of excess zeros by testing the ZINB regression against the standard negative binomial regression. Statistical significance of Vuong’s test indicates that the zero-inflated model would be preferred.
ResultsVariable correlations, means, standard deviations, and internal reliability estimates are provided in Table 1.
Correlations and Descriptive Statistics for Variables of Interest
Death Ideation
Table 2 presents the results for the ZINB regression, with death ideation as the outcome variable. Within the dataset, there were 66 occurrences of zero for death ideation (27.6% of participants). The model with covariates was significant, with Wald χ2 equal to 35.59, p < .001. Vuong’s test (Vuong, 1989) was significant (p < .001), supporting the use of the ZINB. No variables were significantly associated with excess zeros (i.e., nonideators) for death ideation. There was a statistically significant relation between the predictor variables and the level of death ideation. In the negative binomial model, the main effects of perceived burdensomeness (estimate = 0.03, p = .015) and hopelessness (estimate = 0.04, p = .032) were statistically significant, whereas the main effects of depression (estimate = 0.01, p = .092) and thwarted belonging (estimate = 0.00, p = .900) were not.
Zero-Inflated Negative Binomial Regression Results for Death Ideation
Presented in the table are the incidence-rate ratios (IRR). An IRR is the exponent of the parameter estimate and reflects the percentage change in the incidence rate of death ideation associated with a change in the predictor variable, holding the other variables constant. In other words, a 1-unit increase in perceived burdensomeness is associated with an approximately 3.5% increase in the incidence rate of death ideation, holding hopelessness, depression, and thwarted belonging constant. Similarly, a 1-unit increase in hopelessness, holding other variables constant, was associated with a 4.3% increase in death ideation.
Suicide Ideation
The ZINB regression results for suicide ideation are presented in Table 3. Within this dataset, there were 104 occurrences of zero on suicide ideation (43.5% of participants). The model with covariates was significant, with Wald χ2 equal to 113.78, p < .001. Vuong’s test (Vuong, 1989) for the existence of excess zeros was also significant, supporting the use of ZINB regression for this model.
Zero-Inflated Negative Binomial Regression Results for Suicide Ideation
Within the binary logistic regression for zero-inflation, the three predictors significantly associated with nonideation (i.e., excess zeros) were the main effect of thwarted belonging (estimate = −0.11, p = .021), the interaction between perceived burdensomeness and hopelessness (estimate = 0.30, p = .033), and the interaction between perceived burdensomeness and thwarted belonging (estimate = 0.10, p = .038). The main effects of perceived burdensomeness (estimate = −2.57, p = .054) and depression (estimate = −0.09, p = .078), and the three-way interaction between perceived burdensomeness, thwarted belonging, and hopelessness (estimate = −0.03, p = .068) were not significant. The parameter estimates in Table 3 give the log of the change in odds that a participant is a nonideator for a 1-unit increase in the variable; therefore, the exponent of the estimate gives the odds ratio. For example, a 1-unit increase in thwarted belonging conditionally reduces the odds of an individual being a nonideator by 10.5% (whereas a 1-unit decrease in thwarted belonging directly increases the odds by 11.8%). However, if we want to evaluate the total effect of a change in thwarted belonging on the probability of an individual being a nonideator, the predicted value depends upon the level of all covariates.
The predicted probabilities for belonging to the nonideator group given changes in perceived burdensomeness are presented in Figure 1. The curves depicted reflect depressive symptoms held at the mean value for all curves, thwarted belonging at the mean and one standard deviation above the mean, and hopelessness at the mean and one standard deviation above the mean. Figure 2 presents the predicted probabilities of belonging to the nonideator group given changes in thwarted belonging. Again, depression is held at its mean value; hopelessness is at the mean and one standard deviation above the mean; and perceived burdensomeness is at one standard deviation below the mean, at the mean, and one standard deviation above the mean. Above, we noted that the two-way interaction between perceived burdensomeness and hopelessness significantly predicted nonideators. As is evident in Figures 1 and 2, as scores on both perceived burdensomeness and hopelessness simultaneously increase, the probability of being a nonideator (i.e., excess zero) decreases significantly. This suggests that, when individuals report elevated perceptions of being burdens on others and also feel hopeless, these states are likely to change, and the individuals are much more likely to be potential ideators. Likewise, the significant two-way interaction between perceived burdensomeness and thwarted belonging suggests that, as perceived burdensomeness and thwarted belonging simultaneously increase, the probability of being a nonideator decreases significantly. This also suggests that those with higher scores on these variables are much more likely to be potential ideators.
Figure 1. Probability of nonideator status (excess zero) as a function of thwarted belonging and hopelessness along the continuum of scores for perceived burdensomeness. BHS = Beck Hopelessness Scale.
Figure 2. Probability of nonideator status (excess zero) as a function of perceived burdensomeness and hopelessness along the continuum of scores for thwarted belonging. TB = thwarted belonging, BHS = Beck Hopelessness Scale.
As noted above, the tested three-way interaction was not significant (p = .068). This may be due to limited power as a result of sample size. The pattern of results discussed below is based on the parameter estimates obtained from analyses described above; however, we would like to emphasize that the following should be interpreted with caution. Figure 1 depicts the change in probability of being a nonideator as a function of linearly increasing scores on perceived burdensomeness for individuals at differing levels of thwarted belonging and hopelessness. The pattern of results indicates that at lower levels of perceived burdensomeness and mean scores on thwarted belonging, those with hopelessness scores one standard deviation above the mean have a lower probability of being nonideators. Further, for those with thwarted belonging and hopelessness one standard deviation above the mean, the probability of being nonideators is lower. Figure 2 depicts the change in probability of being nonideators as a function of linearly increasing scores on thwarted belonging for individuals at differing reported levels of perceived burdensomeness and hopelessness. The patterns of results suggests that, at lower levels of thwarted belonging, an individual with scores at the mean or one standard deviation above the mean on perceived burdensomeness and hopelessness may have a lower probability of being a nonideator. Taken together, as reported experiences of perceived burdensomeness, hopelessness, and thwarted belonging increase, the probability of the individual being a nonideator (i.e., excess zero) reduces substantially, suggesting that he or she is very likely to be experiencing suicide ideation, even if it is not reported. As with Figure 1, this figure suggests that elevated scores on all three variables are associated with a low probability of an individual being a nonideator (i.e., excess zero). Put another way, elevated scores on perceived burdensomeness, thwarted belonging, and hopelessness are associated with a much greater probability that the individual is experiencing suicide ideation, even if it is not reported.
Within the negative binomial regression portion of the model, none of the predictor variables for suicide ideation were statistically significant, suggesting that variations in these variables do not have a strong association with variations in suicide ideation.
DiscussionA primary contribution of this research has been the use of advanced statistical procedures to identify nonideators (i.e., individuals who deny suicide ideation and who report nonexistent risk on other variables associated with suicide ideation), and potential ideators (i.e., those who deny suicide ideation while simultaneously reporting other established risk factors for suicide), which has significant clinical implications. This study introduced the use of zero-inflated statistical models to the study of suicidal behavior, which offers a wealth of advantages in answering research questions that are relevant to this field. As noted earlier, no research has utilized ZINB analyses to test hypotheses proposed by the interpersonal theory of suicide (Van Orden et al., 2010) in older adults. Our use of more advanced statistical techniques allowed for computation of both the binary logistic and negative binomial regression relations between predictor and outcome variables, as informed by our hypotheses. This allowed us to test theory-based predictions regarding variability in death ideation and suicide ideation, while also predicting participants’ denial of suicide ideation, despite reporting experiences that are associated with suicide risk.
We hypothesized that death ideation would be significantly predicted by perceived burdensomeness, thwarted belonging, hopelessness, and depressive symptoms. Our results indicated that only perceived burdensomeness and hopelessness significantly predicted variation in death ideation in the negative binomial portion of the regression. This analysis was conducted to test the first prediction of Van Orden et al. (2010), that individuals who feel a lack of connection to others, or perceive themselves to be a burden on others, will develop a wish for death. Our results support the association of perceived burdensomeness with death ideation, but do not indicate that thwarted belonging was associated with death ideation. As such, these results provide partial support for the interpersonal theory’s assertion that perceived burdensomeness and thwarted belonging are each associated with death ideation when experienced independent of each other. This may signify that thwarted belonging is exclusively associated with the development of suicide ideation in this population. Although it was surprising that depressive symptoms were not associated with death ideation, participants in this study had low scores on depressive symptoms, which may suggest that older adults in our sample were less likely to report feeling depressed, though they endorsed other psychological experiences. Taken together, the significant relations between perceived burdensomeness and death ideation, as well as between hopelessness and death ideation, may suggest that painful emotional experiences related to perceptions of detracting from others and having little hope for the future are associated with ideations related to death in older adults.
In addition, our pattern of results (though nonsignificant) is consistent with Van Orden et al. (2010)’s prediction that individuals with elevated perceived burdensomeness and thwarted belonging would develop suicide ideation when they feel hopeless that these states will change. These findings provide further support for the interpersonal theory of suicide. Neither the negative binomial regression nor the zero-inflation binary logistic regression that tested this three-way interaction was significant; however, the pattern of results from the logistic regression with zero-inflation indicates that increasing scores on these variables, and especially increasing scores on all three variables simultaneously, are associated with a reduced probability that an individual is a nonideator. Put another way, individuals reporting these states are more likely to be potential ideators. Although these results should be interpreted with caution, this is fascinating in that it may suggest that for older adults, these experiences may be associated with the presence or absence of suicide ideation, but are less important to determining the severity of thoughts of suicide.
It is noteworthy that the correlates of death ideation and suicide ideation were somewhat different. Specifically, none of the predictor variables predicted excess zeros on death ideation, whereas thwarted belonging, the interaction between perceived burdensomeness and hopelessness, and the interaction between perceived burdensomeness and thwarted belonging predicted excess zeros on suicide ideation. Although this is speculative, it may suggest more of a presence/absence relationship among correlates of suicide ideation, whereby experiences such as feeling a thwarted sense of belonging trigger the onset of thoughts of suicide, but do not impact the severity of these thoughts. In contrast, the predictors of death ideation were associated with the severity of death ideation rather than the presence of excess zeros. This suggests that these predictors may not lead to the onset of death ideation (thoughts that are more common among older adults), but instead influence the severity of death ideation. In addition, it was surprising that thwarted belonging was not significantly associated with death ideation in either analysis. This could indicate that thwarted belonging is a psychological state so painful that it is associated with an active wish to take one’s own life, rather than a more passive desire for death.
There are several very important implications to these findings. The results suggest that variables included in the interpersonal theory should be key targets in the determination of whether an older adult might be experiencing thoughts of suicide, regardless of whether the older adult is reporting such thoughts. As we noted above, increasing scores on thwarted belonging, perceived burdensomeness, and hopelessness are all associated with a much greater probability that an individual is experiencing thoughts of suicide, whether or not they are reported. The figures especially highlight this, indicating that the probability is near zero that an individual is not experiencing thoughts of suicide if they are reporting elevated experiences related to these constructs. This information is very important from an assessment perspective. As noted earlier, data suggest that older adults may underreport thoughts of suicide (Heisel et al., 2006); therefore, the identification of variables that are associated with the probability of suicide ideation (but that are not direct questions regarding suicide ideation) may prove invaluable for mental health practitioners and primary care physicians seeking to evaluate suicide risk in older adults who might not directly report suicide ideation. In addition to the assessment implications of these findings, the results of this study indicate that mental health practitioners should target perceptions of being a burden, the sense of thwarted belonging, and hopelessness in older adults in an effort to reduce their risk of developing suicide ideation. This study is the first study to attempt to identify such variables using statistical approaches such as zero-inflated negative binomial regression.
This study, although valuable, has limitations that must be noted. First, the cross-sectional design prevents inference of causal relationships. As such, we have been able to identify variables that are associated with death ideation, as well as patterns of variables that are associated with a decreased probability that an individual is currently not experiencing suicide ideation, but we have not been able to draw conclusions about the role of these variables in the development and maintenance of death ideation or suicide ideation. Longitudinal examinations of the Van Orden et al. (2010) model may provide further elucidation of the relations between variables. Furthermore, investigation of other variables not examined in this study (e.g., acquired capability for suicide, emotion inhibition) may offer additional explanation of the differential increased risk among older adults with greater risk for death by suicide (i.e., older adult men). In addition, limited racial diversity, relatively high education levels, and limited geographic dispersion of the sample all limit generalizability.
In summary, it appears that perceived burdensomeness and hopelessness are critical risk factors for death ideation in older adults. Furthermore, the ZINB results suggest that the experiences of thwarted belonging, perceived burdensomeness, and hopelessness are critical to the identification of older adults who may be experiencing suicide ideation, but are not directly reporting it. Providers should use this information in developing efficient and effective screening methods for suicide ideation, as well as for ensuring accurate assessment of older adults who might underreport to direct questions regarding suicide ideation. Participants who deny a thwarted sense of belonging, perceptions of burdensomeness, and hopelessness are unlikely to be experiencing suicide ideation; however, those who indicate elevated thoughts and emotions related to these variables may be experiencing suicide ideation, at least at a low level, even as they deny it.
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Submitted: November 16, 2012 Revised: September 3, 2013 Accepted: October 7, 2013
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Source: Journal of Abnormal Psychology. Vol. 122. (4), Nov, 2013 pp. 1021-1030)
Accession Number: 2013-44247-008
Digital Object Identifier: 10.1037/a0034953
Record: 49- Title:
- The consequences of depressive affect on functioning in relation to Cluster B personality disorder features.
- Authors:
- Miller, Joshua D.. Department of Psychology, University of Georgia, Athens, GA, US, jdmiller@uga.edu
Gaughan, Eric T.. Department of Psychology, University of Georgia, Athens, GA, US
Pryor, Lauren R.. Department of Psychology, University of Georgia, Athens, GA, US
Kamen, Charles. Department of Psychology, University of Georgia, Athens, GA, US - Address:
- Miller, Joshua D., Department of Psychology, University of Georgia, Athens, GA, US, 30602-3013, jdmiller@uga.edu
- Source:
- Journal of Abnormal Psychology, Vol 118(2), May, 2009. pp. 424-429.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 6
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- Cluster B personality disorders, depressive mood induction, laboratory tasks, functioning
- Abstract:
- The authors examined the effects of depressed affect (DA) on functioning measured by behavioral tasks pertaining to abstract reasoning, social functioning, and delay of gratification in relation to Cluster B personality disorder features (PDs) in a clinical sample. Individuals were randomly assigned to either a DA induction or control condition. Consistent with clinical conceptualizations, the authors expected that Cluster B PD symptoms would be related to maladaptive responding (e.g., poorer delay of gratification) when experiencing DA. As hypothesized, many of the relations between the Cluster B PDs and functioning were moderated by DA (e.g., borderline PD was negatively related to abstract reasoning, but only in the DA condition). However, many of the Cluster B PDs symptom counts were related to more adaptive responses in the DA condition (e.g., less aggressive social functioning, better delay of gratification). The authors speculate that individuals with Cluster B PDs may be more likely to respond maladaptively to alternative negative mood states, such as anger and fear. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Depression (Emotion); *Functional Analysis; *Personality Disorders; *Subtypes (Disorders); Experimental Laboratories; Consequence
- Medical Subject Headings (MeSH):
- Adaptation, Psychological; Adult; Affect; Depression; Diagnostic and Statistical Manual of Mental Disorders; Female; Humans; Male; Neuropsychological Tests; Personality Assessment; Personality Disorders; Psychiatric Status Rating Scales; Risk Assessment; Risk Factors
- PsycINFO Classification:
- Personality Disorders (3217)
- Population:
- Human
Male
Female
Outpatient - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs) - Tests & Measures:
- Positive and Negative Affect Schedule—Expanded Form
Wechsler Adult Intelligence Scale– III, Matrix Reasoning subscale
Hypothetical Money Choice Task
Structured Clinical Interview for DSM-IV Axis II Personality Disorders
Symptom Checklist-90–Revised DOI: 10.1037/t01210-000 - Grant Sponsorship:
- Sponsor: University of Georgia Research Foundation, US
Recipients: Miller, Joshua D. - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Feb 4, 2009; Revised: Feb 4, 2009; First Submitted: Jul 29, 2008
- Release Date:
- 20090504
- Correction Date:
- 20151207
- Copyright:
- American Psychological Association. 2009
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0015684
- PMID:
- 19413417
- Accession Number:
- 2009-06385-017
- Number of Citations in Source:
- 36
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2009-06385-017&site=ehost-live">The consequences of depressive affect on functioning in relation to Cluster B personality disorder features.</A>
- Database:
- PsycINFO
The Consequences of Depressive Affect on Functioning in Relation to Cluster B Personality Disorder Features
By: Joshua D. Miller
Department of Psychology, University of Georgia;
Eric T. Gaughan
Department of Psychology, University of Georgia
Lauren R. Pryor
Department of Psychology, University of Georgia
Charles Kamen
Department of Psychology, University of Georgia
Acknowledgement: This research was supported by a grant from the University of Georgia Research Foundation, awarded to Joshua D. Miller.
Personality disorders (PDs) are associated with “a pattern of inner experience and behavior that deviates markedly from the expectations of the individual’s culture… in two of the following areas”: cognition, affectivity, interpersonal functioning, and impulse control (American Psychiatric Association, 2000, p. 689). Cluster B PDs (i.e., antisocial, borderline, histrionic, narcissistic), in particular, are associated with a number of problematic behaviors, such as deliberate self-harm (Klonsky, Oltmanns, & Turkheimer, 2003) and other behaviors thought to fall on the externalizing continuum, such as aggression (e.g., Westen, Shedler, Durrett, Glass, & Martens, 2003) and substance use (e.g., Trull, Waudby, & Sher, 2004). Despite overlap in some of the behavioral correlates across the Cluster B PDs (e.g., aggression is correlated with all Cluster B PDs), there may be different pathways to these outcomes.
Negative affectivity is one domain that may vary across the Cluster B PDs in terms of both the experience of it (e.g., mean elevations, intensity, lability) and its effect on other domains, such as cognition, interpersonal functioning, and impulse control. For example, individuals with borderline (BPD) and narcissistic (NPD) PDs are thought to behave in an impulsive, risky, and/or aggressive manner when experiencing certain negative affective states. It is important to note, however, that the relevant affective states may differ across these PDs such that anger may be pertinent to both BPD and NPD, whereas depression may be more specific to BPD (e.g., Samuel & Widiger, 2008). In fact, behaving impulsively as the result of negative affect is thought to be pathognomonic to BPD; the Diagnostic and Statistical Manual of Mental Disorders (4th ed., Text Rev.; DSM–IV–TR; American Psychiatric Association, 2000) suggests that individuals with BPD may engage in risky sexual behavior, drive unsafely, overspend, abuse substances, or engage in binge eating when affectively dysregulated. It is interesting to note that BPD is associated with both high mean levels of negative affectivity (e.g., mean r = .49; Saulsman & Page, 2004) and affective instability (e.g., r = .36; Miller & Pilkonis, 2006) such that there are frequent and rapid changes in affective states, such as sadness, fear, and hostility (Trull et al., 2008). It has been suggested that the behaviors associated with BPD, such as deliberate self-harm, may be attempts to express and/or regulate unpleasant affective states (e.g., Brown, Comtois, & Linehan, 2002).
The relations between negative affect and problematic behavioral outcomes is less clear for the remaining Cluster B PDs, as they are not strongly associated with mean levels of negative affectivity (Saulsman & Page, 2004) but are associated with affective instability (Miller & Pilkonis, 2006). For example, although narcissistic individuals are generally aggressive (Reidy, Zeichner, Foster, & Martinez, 2008), these individuals behave more aggressively following an ego threat (e.g., Bushman & Baumeister, 1998), which presumably leads to an increase in negative affective states, such as anger. In contrast, antisocial PD (ASPD) is broadly linked to externalizing behavior due to a combination of higher levels of trait antagonism (Samuel & Widiger, 2008) and disinhibition (Miller & Lynam, 2001); in ASPD, externalizing behaviors may not be as contingent upon affective state.
Although there is ample empirical evidence that Cluster B PDs are related to behaviors such as aggression (e.g., Westen et al., 2003) or self-injury (Klonsky et al., 2003), the causal role of negative affectivity in the relations is unclear. There has been little “in-the-moment” empirical attention paid to the behaviors that individuals with these PDs manifest when distressed. Instead, clinicians and researchers surmise, primarily on the basis of patients’ retrospective recall, that these behaviors (e.g., self-harm) are manifested when these individuals are experiencing emotions such as shame, depression, or anger. It is vital that the relations between affect and behavior be investigated in a manner that allows for an examination of their temporal order. This type of research can be conducted using ecological momentary assessment strategies or using experimental psychopathology paradigms in which a negative mood state is induced.
Experimental paradigms may be particularly helpful, as they provide evidence of the causal nature of the relations among affect and subsequent cognition and behavior, shedding light on potential mechanisms. This methodology is particularly relevant for the study of Cluster B disorders, in which individuals engage in behaviors that might be deemed “self-defeating” in the long term. For example, a recent meta-analytic review of BPD and cognition suggests that “motivational influences and negative affects have a disorganizing influence on executive neurocognitive and memory performance in BPD” (Fertuck, Lenzenweger, Clarkin, Hoermann, & Stanley, 2006, p. 369). The causal role of negative affect on cognition could be tested explicitly in an experimental paradigm in which various forms of negative affect (e.g., sadness, anger) are induced to test whether their induction results in decrements in neurocognitive function for individuals with BPD. Such changes, if they occur, may help explain why these individuals engage in more myopic behavior when experiencing negative affect.
In the current study, we examined whether a depressive affect (DA) induction moderates the relations between Cluster B PDs and abstract reasoning (AR), delay of gratification (DoG), and social functioning using three behavioral tasks. We chose these three broad domains of functioning—cognition, impulsive control, and interpersonal functioning—as they are central to the general DSM–IV criteria for the diagnosis of a PD (American Psychiatric Association, 2000). A DA induction was chosen, as sadness is a frequently experienced aspect of BPD (e.g., Trull et al., 2008) but should be less relevant to the remaining Cluster B PDs (e.g., Samuel & Widiger, 2008). Inducing an affective state thought to be specific to BPD but not the remaining Cluster B PDs allows for a differentiated test of the relations between affect and functioning in relation to these PDs. Ultimately, we expected that all of the Cluster B PDs would demonstrate negative bivariate relations with DoG (e.g., Colvin, Block, & Funder, 1995; Miller et al., in press; Petry, 2002) and positive relations with the use of aggression (e.g., Westen et al., 2003; Saulsman & Page, 2004) in response to hypothetical vignettes depicting interpersonal situations. We also expected ASPD to be negatively related to AR, given the negative relation between antisocial behavior and intelligence (e.g., Lynam, Moffitt, & Stouthamer-Loeber, 1993). In reference to the influence of the DA induction, we expected DA to moderate the relation between BPD and all three areas of functioning such that individuals with BPD features who were experiencing DA would evince poorer AR and DoG and greater aggression. We hypothesized that BPD features would interact with the DA induction to lead to aggression, as negative affect is a significant predictor of aggression (e.g., Berkowitz, 1989). Individuals with BPD features who are in the DA condition should be at a higher risk of aggressive responding given the combination of high trait and state negative affectivity. We did not expect the DA induction to moderate the relations between the remaining Cluster B PDs and the behavioral outcomes, as DA is not a particularly salient affective state in these PDs in comparison with affective states such as anger.
Method Participants
The current study used an outpatient clinical sample of 48 Caucasian individuals (29 women; 19 men; mean age = 31.2 years, SD = 10.5), most of whom had completed some (n = 21; 44%) or all of college (n = 21; 44%). Participants in the two conditions (DA vs. control) did not differ on any relevant demographic variables.
Procedures
Recruitment involved placing advertisements in an outpatient psychology clinic and local newspapers; individuals were screened for eligibility on the basis of the following inclusionary criteria: aged 18–60 years, currently seeing a psychologist or psychiatrist, and absence of psychotic symptoms. Participants completed questionnaires, lab tasks, and a DSM–IV PD interview across two assessments. During the first session, after completing the self-report scales, half the participants took part in an DA induction in which they were asked to write about a time “in which you felt very sad” while listening to a piece of classical music that was played at half speed (e.g., Segal, Gemar, & Williams, 1999). Participants were randomly assigned to the experimental or control condition on the basis of a sequence determined by a random number generator. Affect was assessed prior to and after the mood induction. The order of the tasks immediately following the induction was as follows: abstract reasoning, hypothetical money choice task, and social vignettes. All participants provided informed consent and were debriefed and paid for their participation following completion of the study.
Measures
Structured Clinical Interview for DSM–IV Personality Disorders
The Structured Clinical Interview for DSM–IV Personality Disorders (First, Gibbon, Spitzer, & Williams, 1997) is a semistructured interview that assesses DSM–IV PDs. In the current study, only Cluster B PDs are used. Dimensional PD scores were created by adding the ratings (on a 0 to 2 scale) across symptoms. Alphas ranged from .61 (BPD) to .76 (NPD). Thirteen cases were rated by two judges to examine inter-rater reliability; intraclass correlations ranged from .62 (ASPD) to .78 (histrionic). Mean PD scores were as follows: ASPD (dimensional M = 2.8, SD = 2.9; categorical n = 3), BPD (dimensional M = 3.7, SD = 3.1; categorical n = 3), histrionic (dimensional M = 1.7, SD = 2.0; categorical n = 0), NPD (dimensional M = 3.5, SD = 3.5; categorical n = 1).
Symptom Checklist–90–Revised
The Symptom Checklist–90–Revised (Derogatis, 1975) is a 90-item self-report inventory that assesses a range of current (i.e., within the past 7 days) psychological symptoms; only the Global Severity Index is used in the current study. Scores on the Global Severity Index ranged from 1.08 to 2.80 (M = 1.75, SD = 0.43; α = .97).
Positive and Negative Affect Schedule—Expanded Form (Watson & Clark, 1994)
Items from three scales were used: sadness, joviality, and fatigue. Alphas ranged from .82 to .92. Pre- and postinduction means were as follows: sadness (10.62, SD = 4.2, and 13.29, SD = 4.4, respectively), joviality (15.63, SD = 5.8, and 11.08, SD = 4.6), and fatigue (9.54, SD = 3.75, and 8.83, SD = 3.80).
Abstract reasoning
We administered a shortened version (i.e., 13 of 26 items: items 1, 3, 5, 7, 9, 11, 13, 15, etc.) of the Matrix Reasoning subscale from the Wechsler Adult Intelligence Scale–III (Wechsler, 1997). The mean score was 10.36 (SD =1.65).
Hypothetical Money Choice Task (Rachlin, Raineri, & Cross, 1991)
The Hypothetical Money Choice Task was used as a measure of DoG in that participants were asked to choose between a hypothetical larger amount of money available after a delay (i.e., 1 month) and a smaller hypothetical amount of money that was available immediately. The amount of money available after the delay remained fixed ($1,000), whereas the immediately available amount was varied. Participants repeatedly chose between the larger delayed reward and the immediately available reward, which varied in 30 increments from $1 to $1,000. Participants completed two sets of trials; in one series, the amount of money for the immediate reward increased throughout the trials; in the other series, the amount of money for the immediate reward decreased throughout the trials. The dependent variable is calculated by averaging the values for each of the two sets of trials. The first value is the point at which the participant switched preference from the immediate to the delayed rewards when the immediate rewards were presented in the descending order. The second value is the point at which the participant switched from the delayed to the immediate rewards when the immediate rewards were presented in ascending order. The mean score was $785 (SD = $250).
Social vignettes
Participants read 12 vignettes (Tremblay & Belchevski, 2004) describing a hypothetical interaction in which another person performs a behavior that might be considered provocative to the participant (e.g., “You are at a local dance club. While you are dancing a stranger bumps into you very roughly”); four were “hostile” in nature, four were “ambiguous,” and four were “unintentional.” The participants were then asked questions answered on a 1 (not at all likely) to 11 (extremely likely) scale, which assessed the likelihood of (a) being rude, (b) yelling or swearing, (c) threatening the other person if the situation was not resolved, and (d) using physical force if the situation was not resolved. The answers were summed to yield a total score (M = 103.73, SD = 34.7; α = .87).
ResultsThe DA induction was successful, as indicated by increases in sadness, t(23) = −3.0, p ≤ .01, d = .62, and decreases in joviality, t(23) = 5.80, p ≤ .01, d =.87. As expected, the induction did not affect ratings of fatigue, t(23) = .91, ns, d = .19. Next, we examined the bivariate correlations between the study constructs. Finally, we performed a series of regression analyses in which the outcome variables were regressed on the individual Cluster B dimensional scores, a dichotomous variable representing condition (i.e., induction vs. control), and an interaction term. We centered the PDs and the condition variable prior to creating the interaction term to reduce multicollinearity. Because of the small sample size and the difficulty of finding significant interactions (Aiken & West, 1991), we increased our threshold for statistical significance to p < .10 for the interaction terms. For all other statistical tests, we relied on more traditional significance levels (i.e., p ≤ .05).
Bivariate Relations Between Study Variables
Table 1 provides the interrelations between the study variables. Correlations between the Cluster B PDs ranged from .36 (BPD–histrionic) to .60 (BPD–ASPD), with a median of .46. Correlations between the task variables ranged from −.33 (aggressive social functioning–DoG) to .15 (DoG–AR), with a median of −.15. Condition (experimental vs. control) was uncorrelated with all relevant variables. The Cluster B PDs were positively correlated with aggressive social functioning (rs ranged from .18 to .51) and negatively correlated with DoG (rs ranged from −.24 to −.36). ASPD was the only PD that manifested a significant correlation with AR (r = −.30).
Interrelations Among Study Variables
Cognitive Functioning: Interactions Between Cluster B PDs and DA
As seen in Table 2, there was only one significant PD × Condition interaction for AR, such that BPD symptoms were significantly negatively related to AR in the DA condition (B = −.27, SE =.11, p ≤ .05) but nonsignificantly related in the control condition (B =.09, SE =.10, ns).
Cluster B PDs, DA, and Cognitive and Social Functioning
Delay of Gratification: Interactions Between Cluster B PDs and DA
There was a significant interaction (p ≤ .08) between NPD and condition, such that NPD was significantly negatively related to the size of the monetary amount chosen in the control condition (i.e., narcissistic individuals chose smaller but more immediately available monetary amounts; B = −44.04, SE =14.1, p ≤ .01) but was unrelated to the size of the amount chosen in the DA condition (B = −8.35, SE =13.4, ns).
Social Functioning: Interactions Between Cluster B PDs and DA
There were three significant interactions between the PDs and condition (i.e., BPD, histrionic, and NPD) in the prediction of aggressive social functioning. For all three interactions, the PDs were significantly positively related to aggressive responding in the control condition (BPD B = 7.6, SE = 2.0, p ≤ .01; histrionic B = 9.4, SE = 3.4, p ≤ .01; NPD B = 7.0, SE = 1.9, p ≤ .01) but were unrelated in the DA condition (BPD B = 0.81, SE = 2.15; histrionic B = −3.1, SE = 3.4; NPD B = −0.47, SE = 1.8).
Are the Current Effects Due to Axis I-Related Distress?
To control for the possible effects of psychological distress, we reran the previous regression analyses including the Global Severity Index from the Symptom Checklist–90–Revised. Of the previous 11 significant (or near significant) main effects (6) or interactions (5), 8 remained significant. The change in statistical significance for these three effects once the Global Severity Index was included (i.e., main effect of ASPD on AR; main effect of histrionic on DoG; interaction of NPD and condition on NPD) appeared to be primarily due to a loss of power as the relations remained in the same direction with only small reductions in size.
DiscussionIn the current study, DA had both adaptive and maladaptive effects on the relations between Cluster B PDs and functioning. As hypothesized, BPD was negatively related to abstract reasoning, but only in the DA condition. Previous work has shown that “BPD subjects appear to exhibit non-specific deficits in multiple domains of executive neurocognitive and memory performance in comparison to psychiatric and non-clinical groups” (Fertuck et al., 2006, p. 369). These authors noted that these findings are “tentative,” however, because of the possibility that there may be “state-dependent effects,” which is consistent with the current results. This is significant in that it may partially explain why individuals with BPD symptoms engage in behaviors that may seem counterproductive, dangerous, and/or maladaptive, such as self-harm. Although individuals with BPD symptomatology may engage in these behaviors for a variety of reasons, such as distraction or self-punishment, the potential consequences may be more readily ignored when one is not processing information as clearly because of the presence of DA. This hypothesis warrants further examination given the current evidence for impaired cognitive functioning during periods of DA.
It is worth noting, however, that the DA induction did not differentially affect all areas of functioning in relation to BPD, as one might expect (e.g., BPD was not related to poorer delay of gratification or aggressive social behavior in the DA condition). This was surprising, as BPD is linked with a variety of different forms of impulsivity (e.g., Whiteside, Lynam, Miller, & Reynolds, 2005) and has been linked to risky decision making under normal affective conditions (Kirkpatrick et al., 2007). These findings are not without precedence. Chapman, Leung, and Lynch (2008) found that individuals with BPD symptoms who were currently experiencing negative affectivity performed in a less impulsive manner than did individuals with BPD who were not experiencing concurrent negative affectivity (i.e., d = .74), thus suggesting that certain types of negative emotions may have an inhibitory effect on individuals with BPD symptomatology with regard to certain domains of functioning. As noted by Chapman et al., similar results have been found in studies on psychopathy in which anxiety moderates the relation between psychopathy and passive avoidance errors (e.g., Newman & Schmitt, 1998).
Consistent with Chapman et al.’s (2008) findings with BPD, DA had some adaptive effects on histrionic and NPD (and, to a lesser extent, BPD), in that individuals with these symptoms were better able to delay gratification (i.e., NPD) and reported lower probabilities of acting aggressively in hypothetical social situations when experiencing DA (i.e., BPD, NPD, histrionic). All three of these disorders may be linked to a strong behavioral activation system (Gray, 1987; Foster & Trimm, 2008; Pastor et al., 2007), which suggests that individuals with these disorders or traits may be sensitive to signs of reward and novelty. In the current situation, the induction of DA may have dampened, at least briefly, the strength of the behavioral activation system and/or activated the behavioral inhibition system, which is sensitive to signs of punishment or nonreward (when reward is expected). The current results would suggest that many of the negative behaviors believed to occur in relation to Cluster B PDs and negative affect (e.g., self-harm, aggression) may occur under more activating and arousing affective states, such as anger or anxiety, rather than depression.
As expected (e.g., Moeller et al., 2002; Simonoff et al., 2004), ASPD was related to impaired functioning across the domains, and these effects were not contingent upon DA. Instead, the relations between ASPD and poorer social and intellectual functioning may reflect the influence of stable individual differences in intellect and personality that are unrelated to affectivity. Alternatively, there was some evidence that individuals with ASPD symptoms manifested less change in affect, specifically sadness, as a result of the mood induction (i.e., r = −.40, p < .06). The difficulty of inducing DA in relation to ASPD may have confounded our ability to test these relations. It is possible that a DA induction is likely to fail with individuals with ASPD who experience more externally directed forms of negative affect (e.g., anger) than self-directed forms (e.g., depression; Samuel & Widiger, 2008).
In conclusion, Cluster B PD pathology was related to more problematic functioning (i.e., poorer abstract reasoning in ASPD; poorer delay of gratification in histrionic and NPD; and aggressive responding to social situations in ASPD, NPD, and BPD). However, the induction of DA had a substantial effect on these behaviors, some of which could be viewed as being maladaptive (e.g., poorer abstract reasoning), but several of which could be viewed as being adaptive (e.g., less verbal and physical aggression, greater ability to delay gratification). Given the small size of the current sample, future studies are needed to test the generalizability of these findings. Methodologically, future studies should counterbalance the presentation of the laboratory tasks, as it is possible that the effects of the mood induction varied systematically across the tasks (i.e., stronger for the first task than the final task) because the DA induction may have weakened because of the progression of time or distraction by the preceding tasks. Although we believe that the current mood induction may have been more powerful and long-lasting than most (e.g., Velten statements), this was not explicitly tested and requires empirical examination. In addition, because the current induction resulted in changes in negative and positive affect, we are unable to disentangle their individual effects. Future research should aim to do that, although it may prove difficult to manipulate one without affecting the other (see Westermann, Spies, Stahl, & Hesse, 1996, for a review). It will also be important to test these relations in samples with greater degrees of personality pathology. Finally, it will be important to use similar paradigms to test the effect of the induction of other forms of negative affectivity, such as anger, which may be the most salient and problematic for individuals with Cluster B PD symptoms (e.g., Trull et al., 2008).
Footnotes 1 The findings do not change if gender is included in the regression model at Step 1.
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Submitted: July 29, 2008 Revised: February 4, 2009 Accepted: February 4, 2009
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Source: Journal of Abnormal Psychology. Vol. 118. (2), May, 2009 pp. 424-429)
Accession Number: 2009-06385-017
Digital Object Identifier: 10.1037/a0015684
Record: 50- Title:
- The effects of extraverted temperament on agoraphobia in panic disorder.
- Authors:
- Rosellini, Anthony J.. Center for Anxiety and Related Disorders, Department of Psychology, Boston University, Boston, MA, US, ajrosell@bu.edu
Lawrence, Amy E.. Center for Anxiety and Related Disorders, Department of Psychology, Boston University, Boston, MA, US
Meyer, Joseph F.. Center for Anxiety and Related Disorders, Department of Psychology, Boston University, Boston, MA, US
Brown, Timothy A.. Center for Anxiety and Related Disorders, Department of Psychology, Boston University, Boston, MA, US - Address:
- Rosellini, Anthony J., Center for Anxiety and Related Disorders, Department of Psychology, Boston University, 648 Beacon Street, 6th Floor, Boston, MA, US, 02215-2013, ajrosell@bu.edu
- Source:
- Journal of Abnormal Psychology, Vol 119(2), May, 2010. pp. 420-426.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 7
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- agoraphobia, panic disorder, personality, situational avoidance, temperament, extraverted
- Abstract:
- Although situational avoidance is viewed as the most disabling aspect of panic disorder, few studies have evaluated how dimensions of neurotic (i.e., neuroticism, behavioral inhibition) and extraverted (i.e., extraversion, behavioral activation) temperament may influence the presence and severity of agoraphobia. Using logistic regression and structural equation modeling, we examined the unique effects of extraverted temperament on situational avoidance in a sample of 274 outpatients with a diagnosis of panic disorder with and without agoraphobia. Results showed low extraverted temperament (i.e., introversion) to be associated with both the presence and the severity of situational avoidance. Findings are discussed in regard to conceptualizations of conditioned avoidance, activity levels, sociability, and positive emotions within the context of panic disorder with agoraphobia. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Agoraphobia; *Avoidance; *Extraversion; *Panic Disorder; *Personality
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Aged; Agoraphobia; Anxiety; Chi-Square Distribution; Extraversion (Psychology); Female; Humans; Male; Middle Aged; Models, Psychological; Panic Disorder; Personality Assessment; Psychiatric Status Rating Scales; Regression Analysis; Surveys and Questionnaires; Temperament
- PsycINFO Classification:
- Neuroses & Anxiety Disorders (3215)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs)
Aged (65 yrs & older) - Tests & Measures:
- Anxiety Disorders Interview Schedule for DSM-IV: Lifetime Version
Behavioral Inhibition Scale/Behavioral Activation Scale
NEO Five-Factor Inventory
Albany Panic and Phobia Questionnaire DOI: 10.1037/t12041-000
Anxiety Sensitivity Index DOI: 10.1037/t00033-000 - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- Accepted: Nov 19, 2009; Revised: Nov 18, 2009; First Submitted: Jul 22, 2009
- Release Date:
- 20100510
- Correction Date:
- 20160714
- Copyright:
- American Psychological Association. 2010
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0018614
- PMID:
- 20455614
- Accession Number:
- 2010-08841-017
- Number of Citations in Source:
- 41
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- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2010-08841-017&site=ehost-live">The effects of extraverted temperament on agoraphobia in panic disorder.</A>
- Database:
- PsycINFO
The Effects of Extraverted Temperament on Agoraphobia in Panic Disorder
By: Anthony J. Rosellini
Center for Anxiety and Related Disorders, Department of Psychology, Boston University;
Amy E. Lawrence
Center for Anxiety and Related Disorders, Department of Psychology, Boston University
Joseph F. Meyer
Center for Anxiety and Related Disorders, Department of Psychology, Boston University
Timothy A. Brown
Center for Anxiety and Related Disorders, Department of Psychology, Boston University
Acknowledgement:
Panic disorder (PD) involves various maladaptive cognitive and behavioral responses. Among the most impairing behavioral responses to panic are interoceptive, experiential, and situational avoidance tactics. Interoceptive avoidance involves refusing substances (e.g., caffeine) or activities (e.g., exercise) that elicit panic-like symptoms. Experiential avoidance refers to attempts to control panic via medications or distraction. Situational avoidance, which has been described as “the most palpable and impairing aspect of PD” (White, Brown, Somers, & Barlow, 2006, p. 148), involves a refusal to enter or tendency to escape from feared environments (e.g., bridges, crowds, elevators).
The Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; DSM–IV–TR; American Psychiatric Association, 2000) describes agoraphobia (AG) as anxiety linked to situations from which escape might be difficult or help may be unavailable in the event of panic symptoms. As fear of being in certain situations is often accompanied by a refusal to enter situations, situational avoidance is an important AG criterion. Because AG is most frequently diagnosed as comorbid with PD in clinical settings (i.e., PD with AG; Brown, Campbell, Lehman, Grisham, & Mancill, 2001), it is no surprise that conceptual models of AG have been strongly influenced by PD theories (e.g., Barlow, 2002).
Temperament, Anxiety Sensitivity, and AGResearch and theory has implicated genetically based dimensions of neurotic temperament (NT) and extraverted temperament (ET) as being instrumental in the etiology and maintenance of anxiety and mood disorders (e.g., Barlow, 2002; Clark, Watson, & Mineka, 1994). Theories of emotion and personality vulnerabilities have described NT and ET by constructs such as neuroticism and extraversion (Digman, 1990; Eysenck & Eysenck, 1985), negative and positive affect (Tellegen, 1985), and behavioral inhibition and activation (Gray, 1987). Although their interrelationships are not yet fully understood, evidence suggests that neuroticism is closely related to negative affect and behavioral inhibition, whereas extraversion shares many characteristics with positive affect and behavioral activation (Barlow, 2002; Brown, 2007; Campbell-Sills, Liverant, & Brown, 2004). Whereas NT influences the experience of negative emotional states (i.e., anxiety, sadness), ET is related to sociability, levels of activity, reward-seeking behaviors, and positive emotions (i.e., excitement, joy).
Contemporary conceptualizations of the relationships between temperament and the emotional disorders stem from the tripartite model, which posited that NT (i.e., negative affect, neuroticism) is relevant to both the anxiety and the mood disorders, whereas ET (i.e., positive affect, extraversion) is uniquely related to depression (Clark & Watson, 1991). Although research has consistently found strong positive correlations between NT and the full range of emotional disorders (Bienvenu et al., 2001, 2004; Brown, 2007; Brown, Chorpita, & Barlow, 1998), findings regarding ET have been limited and mixed. For example, although initial support for the unique association between ET and depression was found in some nonclinical samples (Joiner, 1996) and samples with low rates of anxiety (Watson et al., 1995), examinations of outpatient and epidemiological data also found significant inverse relationships between ET (i.e., high introversion) and social phobia (e.g., Bienvenu et al., 2001; Brown et al., 1998). As subsequent research further supported this relationship (for a meta-analytic review, see Kashdan, 2007), leading conceptual models of the emotional disorders have been revised to reflect such findings (e.g., Mineka, Watson, & Clark, 1998).
Although the evidence is sparse, significant associations have been found between dimensions of ET and AG. For example, Bienvenu et al. (2001) used logistic regression to examine if ET (i.e., extraversion) predicted lifetime prevalence of various DSM anxiety and mood disorders. Results showed that ET was a significant predictor of AG, whereby lower levels ET (i.e., high introversion) were associated with increased odds of a lifetime AG diagnosis. Significant associations between ET and PD were not found. Although studies have had success in replicating and extending these findings (e.g., Bienvenu et al., 2004), few have accounted for the occurrence of AG secondary to PD (e.g., PD with AG). A notable exception is Carrera et al.'s (2006) study of personality traits among patients in the early phases of PD, which controlled for comorbidity between PD and AG. Results showed that ET (i.e., introversion) predicted a diagnosis of PD with AG but not PD without AG. The authors interpreted this finding to indicate that low levels of ET may contribute to the development of AG within PD but not PD itself.
Although compelling, these studies provide limited information about the relationship between ET and AG by exclusively examining DSM diagnostic status. The degree of impairment assumed to be caused by situational avoidance (e.g., White et al., 2006) suggests it may be more important to study avoidance behaviors within AG rather than broadly studying the presence of the disorder. Moreover, exclusively examining dichotomous representations of dimensional phenomena (i.e., diagnoses) provides limited utility by not capturing important information (cf. Brown & Barlow, 2005; MacCallum, Zhang, Preacher, & Rucker, 2002) such as individual differences in AG severity.
Preliminary evidence regarding the relationship between ET and AG has been useful in examining genetic relationships between ET and AG. Recently, Bienvenu, Hettema, Neale, Prescott, and Kendler (2007) used a large twin sample to test the independent genetic contributions of ET and NT (i.e., extraversion and neuroticism) on heritable influences (i.e., genetic vs. shared environmental factors) of AG. Analyses found significant negative within-person correlations between extraversion and AG and that monozygotic twins had higher cross-twin correlations than did dizygotic twins. In other words, the genetic factors that influence extraversion are the same as those affecting a lifetime diagnosis of AG.
In addition to ET and NT, conceptualizations of PD and AG also emphasize the construct of anxiety sensitivity (AS), or the fear of anxiety and anxiety-related physical symptoms. Much like ET and NT, AS may be a heritable vulnerability playing an important role in PD and AG (Stein, Jang, & Livesley, 1999). It is posited that high AS may develop early in life and, coexisting with high levels of NT, may lead to the onset and maintenance of PD with or without AG (Barlow, 2002). This model has received support, as individuals with heightened levels of AS experience a greater degree of panic symptoms (Zinbarg, Brown, Barlow, & Rapee, 2001) and agoraphobic fear and avoidance (Taylor & Rachman, 1992; White et al., 2006). Unfortunately, these studies have not evaluated the unique contributions of AS while controlling for NT.
Although the negative consequences of AG within PD have been well documented, relatively few studies have focused on the relationship between ET and situational apprehension and avoidance. Extant studies have rarely examined ET and AG in clinical samples or contained AG symptom information beyond diagnostic status (e.g., Bienvenu et al., 2001, 2004; Carrera et al., 2006). Moreover, much of the literature examining PD and AG has not controlled for levels of NT and AS (e.g., Taylor & Rachman, 1992; White et al., 2006). The present study aims to examine the unique effects of ET on agoraphobic avoidance in PD within a clinical sample. ET was hypothesized to predict the presence and severity of agoraphobic avoidance while controlling for NT and AS. It was also hypothesized that ET would predict the severity of AG but not be associated with the severity of PD.
Method Participants
The sample consisted of 274 patients presenting for assessment and treatment at the Center for Anxiety and Related Disorders at Boston University. The sample was predominantly female (60.2%) and the average age was 32.88 years (SD = 10.56, range = 18–77). The majority of participants self-identified as Caucasian (85.8%). Individuals were assessed by doctoral students or doctoral-level clinical psychologists using the Anxiety Disorders Interview Schedule for DSM-IV: Lifetime Version (ADIS–IV–L; Di Nardo, Brown, & Barlow, 1994). The ADIS–IV–L is a semistructured interview that assesses DSM–IV (American Psychiatric Association, 2000) anxiety, mood, somatoform, and substance use disorders. When administering the ADIS–IV–L, clinicians assign each diagnosis a 0–8 clinical severity rating that represents the degree of distress or impairment in functioning associated with specific diagnoses. The disorder receiving the highest clinical severity rating is considered an individual's principal diagnosis. Patients were included in the study if they met criteria for a principal diagnosis of PD with AG (n = 260) or PD without AG (n = 14). The ADIS–IV–L has shown good to excellent reliability for the majority of anxiety and mood disorders, including PD with AG (κ = .77) and PD without AG (κ = .72; Brown, Di Nardo, Lehman, & Campbell, 2001). Study exclusionary criteria were current suicidal or homicidal intent and/or plan, psychotic symptoms, or significant cognitive impairment (e.g., dementia, mental retardation).
Regression and Structural Model Indicators
ADIS–IV–L PD criteria ratings
Clinicians made severity ratings for the following DSM-IV PD criteria on a 0 (absent) to 8 (very severely disturbing/disabling) scale: (a) recurrent and unexpected panic attacks, (b) fear of having additional attacks, (c) worry about the consequences of panic, and (d) change in behavior related to the panic. A composite score composed of ratings of items (a) through (c) was generated for each participant. Rating (d) was omitted from the composite score because of redundancy with indicators of AG (i.e., situational avoidance would be considered a significant change in behavior).
ADIS–IV–L situational avoidance ratings
The AG section of the ADIS–IV–L contains a subsection in which clinicians assess and rate the patient's avoidance of 22 situations associated with PD (e.g., public transportation, theaters) from 0 (no avoidance) to 8 (very severe avoidance). The AG rating score has been associated with excellent interrater reliability (Brown, Di Nardo, et al., 2001). The AG scale structure was evaluated using exploratory factor analysis. Although the exploratory factor analysis confirmed unidimensionality, one item had a factor loading that was less than .30 (Item 14, “Being home alone”) and was removed from the composite rating.
Albany Panic and Phobia Questionnaire (APPQ; Rapee, Craske, & Barlow, 1994–1995)
The APPQ is a 27-item questionnaire measuring interoceptive, situational, and social fears. Respondents rate how much fear they would experience in certain activities and situations on a 0 (no fear) to 8 (extreme fear) scale. The nine-item Agoraphobia subscale (APPQ-A), measuring situational apprehension commonly associated with panic (e.g., driving, theaters), and the five-item Interoceptive subscale (APPQ-I), assessing fear associated with activities or objects that may mimic panic symptoms, were used in this study. Evaluation of the APPQ supports its factor structure, reliability, and validity in clinical samples (Brown, White, & Barlow, 2005).
Anxiety Sensitivity Index (ASI; Peterson & Reiss, 1992)
The ASI is a 16-item measure in which patients rate each item on a 0 (very little) to 4 (very much) scale. The ASI has adequate reliability and validity and is composed of a hierarchical factor structure, with three lower order factors (i.e., Physical Concerns, Mental Incapacitation, and Social Concerns) and a single general higher order factor (Zinbarg, Barlow, & Brown, 1997).
Behavioral Inhibition Scale/Behavioral Activation Scale (BIS/BAS; Carver & White, 1994)
The BIS/BAS is a 20-item self-report instrument designed to assess Gray's (1987) personality constructs of behavioral inhibition and activation. Items are rated on a 4-point Likert-type scale, ranging from 1 (quite untrue of you) to 4 (quite true of you). The BIS/BAS has demonstrated excellent psychometric properties in clinical samples (Campbell-Sills et al., 2004).
NEO Five-Factor Inventory (NEO-FFI; Costa & McCrae, 1992)
The NEO-FFI is a 60-item self-report inventory that assesses dimensions of the five-factor model of personality: Neuroticism, Extraversion, Openness, Agreeableness, and Conscientiousness. Items are rated on 5-point Likert-type scale, which ranges from 0 (strongly disagree) to 4 (strongly agree). The NEO-FFI is the abbreviated form the NEO-PI-R, a widely used self-report personality measure that has demonstrated excellent reliability and validity (Costa & McCrae, 1992).
Analytic plan
Logistic regression and structural models were evaluated in Mplus 5.2 (Muthén & Muthén, 1998–2009). Missing data were handled by direct maximum likelihood estimation. Model fit was examined using the root mean square error of approximation (RMSEA) and its test of close fit (C-Fit), the Tucker–Lewis index (TLI), the comparative fit index (CFI), and the standardized root mean square residual (SRMR). Guidelines defined by Hu and Bentler (1999) were used in determining acceptable fit (i.e., RMSEA near or below .06, C-Fit above .05, TLI and CFI near or above .95, SRMR near or below .08). Multiple goodness-of-fit parameters were evaluated to examine various aspects of model fit (i.e., absolute fit, parsimonious fit, fit relative to the null). Unstandardized and completely standardized solutions were examined to evaluate the significance and strength of parameter estimates. Standardized residuals and modification indices were used to determine the presence of any localized areas of strain in the solution.
Results Logistic Regression Models
We conducted logistic regression analyses to examine if ET uniquely predicted the presence of situational avoidance within PD patients while controlling for NT and AS. Situational avoidance was defined as having a secondary AG diagnosis and an ADIS–IV–L situational avoidance rating above 0 (n = 222) or not (n = 29; 23 cases were excluded because of missing questionnaires). Two regression models were examined such that the presence of situational avoidance was regressed onto constructs representing dimensions of temperament (i.e., NEO-FFI and BIS/BAS) and AS. As shown in Table 1, only the Extraversion subscale was found to significantly predict the presence of situational avoidance (B = −0.07, p < .05) in the NEO-FFI and AS model. Lower levels of ET (i.e., higher introversion) were associated with increased odds of agoraphobic avoidance (odds ratio = .94, 95% confidence interval [.87–.99]). The regression coefficient for the BAS scale approached statistical significance (B = −0.06, p = .10) in the BIS/BAS and AS model.
Logistic Regression Models Evaluating the Relationship Between Temperament Constructs and the Presence of Situational Agoraphobic Avoidance
Structural Equation Models
Structural regression models were fit to the data to examine the unique association between dimensions of ET and AG. The BAS and NEO–Extraversion subscales were used as indicators for a latent variable representing ET, whereas BIS and NEO–Neuroticism were specified to load on the NT factor. AS was defined solely by ASI–Physical Concerns because of its theoretical relevance specific to PD and AG (Zinbarg et al., 2001). A latent variable representing dimensions of AG was composed of the APPQ-A subscale and ADIS–IV–L AG situational avoidance rating. The APPQ-I subscale and ADIS–IV–L PD criteria composite rating (see the Method section) were used as indicators to represent the latent variable of PD.
Two structural models were evaluated, whereby latent representations of AG (Model 1) and PD (Model 2) were regressed onto dimensions of NT, ET, and AS. Measurement models of the temperament and disorder constructs were not separately evaluated because both models were structurally just identified. Initial inspections of the models revealed that model fit could be improved if a correlated error was estimated between the NEO–Extraversion and NEO–Neuroticism subscales (Model 1 and 2 modification indices = 14.16 and 13.79, respectively). The models were subsequently specified to reflect this method variance shared between the NEO subscales.
It was predicted that when NT and AS were held constant, ET would demonstrate an inverse and statistically significant structural path to AG but not PD. Model 1 fit the data well, χ2(8) = 18.286, p < .05, SRMR = 0.03, RMSEA = 0.06 (C-Fit p = .20), TLI = 0.94, CFI = .97. Figure 1A shows the completely standardized estimates from this solution. In total, AS, NT, and ET explained 29% of the variance in AG. ET uniquely explained a significant portion of the variance in AG (γ = −.31, p < .001) while controlling for AS and NT. The regression paths for AS and NT were also significant; both predictors demonstrated a positive relationship with AG (γs = .21 and .26, respectively; ps < .01).
Figure 1. Latent structural models of the relationship between dimensions of agoraphobia, panic disorder, temperament, and anxiety sensitivity. A: Model 1. B: Model 2. AG = agoraphobia; PD = panic disorder; ET = extraverted temperament; NT = neurotic temperament; AS = anxiety sensitivity. Completely standardized estimates are shown. * p < .01. ** p < .001.
Figure 1B shows the completely standardized estimations from Model 2, which also fit the data well, χ2(8) = 13.681, p = .09, SRMR = 0.03, RMSEA = 0.05 (C-Fit p = .43), TLI = 0.96, CFI = .98. AS, NT, and ET accounted for 69% of the variance in PD. Consistent with prediction, there was not a significant path between ET and PD (γ = −.14, ns). However, AS and NT each uniquely predicted a significant portion of the variance in PD (γs = .63 and .31, ps < .001 and < .01, respectively).
DiscussionConsistent with hypotheses and prior research (i.e., Bienvenu et al., 2001; Carrera et al., 2006), results from the logistic regression analyses showed ET constructs to uniquely predict (NEO–Extraversion) or have trends toward predicting (BAS) the presence of situational avoidance among PD patients while controlling for NT and AS. Structural modeling confirmed that ET was inversely and significantly related to dimensions of AG but not PD. The present study adds to literature on ET and AG conducted at the diagnostic level (i.e., Bienvenu et al., 2001; Carrera et al., 2006) by specifically examining the presence and severity of situational agoraphobic avoidance, arguably the most disabling aspect of PD with AG (White et al., 2006).
In general, ET was associated with both the presence and the severity of situational avoidance among individuals with PD. These results add to the findings of Carrera et al. (2006) by showing that ET may have a more circumscribed relationship with situational avoidance rather than being broadly related to a diagnosis of AG. In line with a predispositional relationship between ET and AG (cf. Brown, 2007; Clark et al., 1994), theory on temperament and aversive conditioning has posited that introverted individuals perceive unconditioned stimuli as subjectively stronger and consequently more reinforcing (Eysenck & Eysenck, 1985). In other words, introverted individuals who experience recurrent and unexpected panic attacks may be more prone to associate their panic symptoms with concurrent stimuli (i.e., the environment), leading them to develop AG characterized by greater situational avoidance. Activation levels, reward-seeking behaviors, and sociability may also play a role; AG may reflect a premorbid disposition toward low activity or reward seeking (i.e., low ET) expressed in the context of unexpected panic, or discomfort or disinterest (i.e., low ET) in being around others when experiencing a vulnerable emotional state like panic. Indeed, the relevance of ET in approach–avoidance motivation and reward-seeking behaviors has been theorized (i.e., introverts are less likely to find novel environments exciting or enjoyable; Eysenck & Eysenck, 1985) and supported in laboratory studies (cf. Robinson, Meier, & Vargas, 2005). Positive emotionality may also have an influence on AG, as individuals prone to experiencing low levels of positive emotions (i.e., low levels of ET) may have difficulty distinguishing the source of the similar physiological symptoms of panic and positive emotions (i.e., increased heart rate due to panic vs. excitement). Through interoceptive fear conditioning principles (i.e., McNally, 1990), the physiological symptoms of positive emotions may serve as a panic trigger. Along these lines, Williams, Chambless, and Ahrens (1997) found that fears of positive emotions (and anger) predicted fear of laboratory-induced bodily sensations in a nonclinical sample.
Conversely, the present findings may also reflect other types of relationships between ET and AG. For instance, according to a complication/scar model (cf. Brown, 2007; Clark et al., 1994), the presence of AG may cause reductions in ET. In other words, developing increasingly severe situational avoidance may lead individuals to be less active and sociable, seek fewer rewards, and experience fewer positive emotions. It is also possible that low ET and AG reflect similar underlying processes, regardless of one's experience of panic. Perhaps introversion is avoidant behavior, with AG serving as expression of this temperament in the context of unexpected panic. Unfortunately, the cross-sectional and correlational nature of the present study precluded our ability to disentangle predispositional, complication/scar, or tautological interpretations.
Although not an a priori aim of the study, findings supporting the effects of AS and NT on PD and AG are consistent with theory (i.e., Barlow, 2002) and add to the extant literature on these vulnerabilities, which has rarely examined either AS or NT while controlling for the other (e.g., White et al., 2006). Given the past debate over the discriminant and incremental validity of AS over NT (Lilienfeld, Jacob, & Turner 1989), it is interesting that both NT and AS significantly predicted dimensions of AG and PD in the structural models. Thus, despite any phenotypic overlap in NT and AS among patients with AG and PD (e.g., experiencing negative affect in response to negative affect, or anxiety focused on fear), both constructs explain a unique portion of the variance in AG and PD.
Despite strengths in methodology (i.e., analyses conducted in a latent variable framework, use of self-report and clinician-rated indicators) and sampling (i.e., large clinical sample), the present study has some limitations. For example, the APPQ-I provides limited information about a single dimension of PD. Although the APPQ-I assesses common behavioral changes related to PD (i.e., avoidance of caffeine), a questionnaire assessing broader dimensions of panic, such as panic frequency and fear (e.g., the Panic Disorder Severity Scale—Self-Report; Houck, Spiegel, Shear, & Rucci, 2002), may have been more appropriate. Another limitation is the predominate representation of Caucasians in the study. Additional research on more diverse samples is needed to examine whether the relationship between ET and AG generalizes to other cultural groups. Finally, the sample may have benefited from additional cases with a diagnosis of PD without AG. Further study of PD without AG may aid in distinguishing features uniquely associated with the development of AG within the context of PD.
Many individuals with PD experience profound disability through persistent avoidance of the situations they associate with panic. Although results of the present study provide meaningful information to the body of literature examining ET and AG, additional research is needed to further examine etiological and maintenance factors of AG. For example, longitudinal research following individuals from premorbid periods to early phases of PD is needed to clarify the relationship between ET and AG (e.g., does low ET cause AG or vice versa?). In addition, experimental research examining the experience of positive emotions in anxiety disorders may aid in the understanding of ET's relevance to disorders such as social phobia and AG.
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Submitted: July 22, 2009 Revised: November 18, 2009 Accepted: November 19, 2009
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Source: Journal of Abnormal Psychology. Vol. 119. (2), May, 2010 pp. 420-426)
Accession Number: 2010-08841-017
Digital Object Identifier: 10.1037/a0018614
Record: 51- Title:
- The predictive utility of a brief kindergarten screening measure of child behavior problems.
- Authors:
- Racz, Sarah Jensen. Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, MD, US, sracz@jhsph.edu
King, Kevin M.. Department of Psychology, University of Washington, WA, US
Wu, Johnny. Department of Statistics, University of Florida, FL, US
Witkiewitz, Katie. Department of Psychology, Washington State University, WA, US
McMahon, Robert J.. Department of Psychology, Simon Fraser University, Burnaby, BC, Canada - Institutional Authors:
- The Conduct Problems Prevention Research Group
- Address:
- Racz, Sarah Jensen, Johns Hopkins Bloomberg School of Public Health, Department of Mental Health, 624 North Broadway, HH Room 808, Baltimore, MD, US, 21205, sracz@jhsph.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 81(4), Aug, 2013. pp. 588-599.
- NLM Title Abbreviation:
- J Consult Clin Psychol
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- 12
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- US : American Psychological Association
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- Journal of Consulting Psychology
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- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- TOCA-R, child behavior problems, screening methods, kindergartners, Teacher Observation of Classroom Adaptation–Revised, at risk populations
- Abstract:
- Objective: Kindergarten teacher ratings, such as those from the Teacher Observation of Classroom Adaptation–Revised (TOCA-R), are a promising cost- and time-effective screening method to identify children at risk for later problems. Previous research with the TOCA-R has been mainly limited to outcomes in a single domain measured during elementary school. The goal of the current study was to examine the ability of TOCA-R sum scores to predict outcomes in multiple domains across distinct developmental periods (i.e., late childhood, middle adolescence, late adolescence). Method: We used data from the Fast Track Project, a large multisite study with children at risk for conduct problems (n = 752; M age at start of study = 6.55 years; 57.7% male; 49.9% Caucasian, 46.3% African American). Kindergarten TOCA-R sum scores were used as the predictor in regression analyses; outcomes included school difficulties, externalizing diagnoses and symptom counts, and substance use. Results: TOCA-R sum scores predicted school outcomes at all time points, diagnosis of ADHD in 9th grade, several externalizing disorder symptom counts, and cigarette use in 12th grade. Conclusions: The findings demonstrate the predictive utility of the TOCA-R when examining outcomes within the school setting. Therefore, these results suggest the 10-item TOCA-R may provide a quick and accurate screening of children at risk for later problems. Implications for prevention and intervention programs are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *At Risk Populations; *Behavior Problems; *Kindergartens; *Screening; Adolescent Development; Rating; Teachers
- Medical Subject Headings (MeSH):
- Adolescent; Attention Deficit and Disruptive Behavior Disorders; Child; Child Behavior Disorders; Child, Preschool; Female; Follow-Up Studies; Humans; Male; Predictive Value of Tests; Psychiatric Status Rating Scales
- PsycINFO Classification:
- Classroom Dynamics & Student Adjustment & Attitudes (3560)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Childhood (birth-12 yrs)
Preschool Age (2-5 yrs)
School Age (6-12 yrs)
Adulthood (18 yrs & older) - Tests & Measures:
- Computerized Diagnostic Interview Schedule for Children
School Adjustment Scale-Child Report Version
School Adjustment Scale-Parent Version
Tobacco, Alcohol and Drugs Measure
Self-Administered Youth Questionnaire
National Longitudinal Survey of Youth 1997
Teacher Social Competence Scale
Child Behavior Checklist
Teacher Observation of Classroom Adaptation--Revised DOI: 10.1037/t31163-000
Teacher's Report Form DOI: 10.1037/t02066-000 - Grant Sponsorship:
- Sponsor: National Institute of Mental Health
Grant Number: R18 MH48043, R18 MH50951, R18 MH50952, R18 MH50953
Recipients: No recipient indicated
Sponsor: National Institute on Drug Abuse
Grant Number: 1 RC1 DA028248-01
Recipients: No recipient indicated
Sponsor: Center for Substance Abuse Prevention
Other Details: Fast Track
Recipients: No recipient indicated
Sponsor: National Institute on Drug Abuse
Other Details: Fast Track
Recipients: No recipient indicated
Sponsor: Department of Education
Grant Number: S184U30002
Recipients: No recipient indicated
Sponsor: National Institute of Mental Health
Grant Number: K05MH00797, K05MH01027
Recipients: No recipient indicated
Sponsor: National Institute on Drug Abuse
Grant Number: DA16903, DA015226, DA017589
Recipients: No recipient indicated
Sponsor: Child & Family Research Institute
Other Details: Establishment Award
Recipients: McMahon, Robert J. - Conference:
- Association for Psychological Science annual meeting, May, 2010, Boston, MA, US
- Conference Notes:
- A preliminary version of this article was presented at the aforementioned conference.
- Methodology:
- Empirical Study; Longitudinal Study; Interview; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Apr 1, 2013; Accepted: Feb 12, 2013; Revised: Sep 11, 2012; First Submitted: Oct 16, 2011
- Release Date:
- 20130401
- Correction Date:
- 20140915
- Copyright:
- American Psychological Association. 2013
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0032366
- PMID:
- 23544679
- Accession Number:
- 2013-11001-001
- Number of Citations in Source:
- 56
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-11001-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2013-11001-001&site=ehost-live">The predictive utility of a brief kindergarten screening measure of child behavior problems.</A>
- Database:
- PsycINFO
The Predictive Utility of a Brief Kindergarten Screening Measure of Child Behavior Problems
By: Sarah Jensen Racz
Department of Mental Health, Johns Hopkins Bloomberg School of Public Health;
Kevin M. King
Department of Psychology, University of Washington
Johnny Wu
Department of Statistics, University of Florida
Katie Witkiewitz
Department of Psychology, Washington State University
Robert J. McMahon
Department of Psychology, Simon Fraser University, Burnaby, British Columbia, Canada, and the Child & Family Research Institute, Vancouver, British Columbia, Canada
Acknowledgement: This work was supported by National Institute of Mental Health (NIMH) Grants R18 MH48043, R18 MH50951, R18 MH50952, R18 MH50953, and by National Institute on Drug Abuse (NIDA) Grant 1 RC1 DA028248-01. The Center for Substance Abuse Prevention and NIDA also provided support for Fast Track through a memorandum of agreement with the NIMH. This work was also supported in part by Department of Education Grant S184U30002, NIMH Grants K05MH00797 and K05MH01027, and NIDA Grants DA16903, DA015226, and DA017589. Preparation of the manuscript was also supported by an Establishment Award from the Child & Family Research Institute awarded to Robert J. McMahon. A preliminary version of this article was presented at the Association for Psychological Science annual meeting, Boston, MA, May 2010.
Members of the Conduct Problems Prevention Research Group, in alphabetical order, include Karen L. Bierman, Department of Psychology, Pennsylvania State University; John D. Coie, Duke University; Kenneth A. Dodge, Center for Child and Family Policy, Duke University; Mark T. Greenberg, Department of Human Development and Family Studies, Pennsylvania State University; John E. Lochman, Department of Psychology, The University of Alabama; Robert J. McMahon, Department of Psychology, Simon Fraser University, and the Child & Family Research Institute; and Ellen E. Pinderhughes, Department of Child Development, Tufts University.
Karen L. Bierman, John D. Coie, Kenneth A. Dodge, Mark T. Greenberg, John E. Lochman, and Robert J. McMahon are the developers of the Fast Track curriculum and have a publishing agreement with Oxford University Press. Mark T. Greenberg is an author on the PATHS curriculum and has a royalty agreement with Channing-Bete, Inc. He is a principal in PATHS Training, LLC. Robert J. McMahon is a coauthor of Helping the Noncompliant Child and has a royalty agreement with Guilford Publications, Inc.; he is also a member of the Treatments That Work Scientific Advisory Board with Oxford University Press.
We are grateful for the collaboration of the school districts that participated in the Fast Track project as well as the hard work and dedication of the many staff members who implemented the project, collected the evaluation data, and assisted with data management and analyses. For additional information concerning Fast Track, see http://www.fasttrackproject.org.
Early behavior problems (e.g., aggressiveness, disruptiveness, oppositionality) are widely recognized as a risk factor for later violence and antisocial behavior (Bennett & Offord, 2001; Campbell, Shaw, & Gilliom, 2000). These early problematic behaviors are associated with several severe negative outcomes in later adolescence and adulthood, including school dropout and unemployment (Fergusson & Horwood, 1998; Jessor, 1998; Loeber & Dishion, 1983). Additionally, aggressive and disruptive behavior identified as early as kindergarten has been shown to predict later delinquent behavior and substance use (Hill, Lochman, Coie, Greenberg, & the Conduct Problems Prevention Research Group [CPPRG], 2004; Petras, Chilcoat, Leaf, Ialongo, & Kellam, 2004; Petras et al., 2005), suggesting that problem behaviors observed early in childhood can persist across development (Loeber, 1982; Loeber & Hay, 1997; Moffitt, 1993). Thus, early intervention aimed at high-risk children is important (Lochman & CPPRG, 1995), and the use of accurate and reliable screening tools to identify children for these targeted prevention programs is essential (Jones, Dodge, Foster, Nix, & CPPRG, 2002).
Brief screening measures distributed to parents, teachers, and other caregivers are often used as an initial step in identifying children who will benefit from prevention programs targeting early behavior problems (CPPRG, 1999; Feil, Walker, & Severson, 1995). Studies examining the predictive utility of these screening tools showed that combining parent and teacher ratings of behavior problems during kindergarten predicted more difficulties in interactions with peers and teachers (Wehby, Dodge, Valente, & CPPRG, 1993), higher levels of delinquency (Hill et al., 2004), and lower levels of social competence (Lochman & CPPRG, 1995) in first grade. Additionally, parent and teacher ratings of child aggressiveness and hyperactivity in preschool predicted child behavior problems at the end of preschool (Doctoroff & Arnold, 2004) and 5 years later (Stormont, 2000). However, these multireporter screening measures can be costly and time-consuming. It is therefore important to examine the predictive utility of brief single-rater screening measures, as these may represent a cost- and time-efficient method to identify children at risk for later behavior problems and associated negative outcomes.
Teacher Ratings of Early Behavior ProblemsTeacher ratings are a promising resource for the implementation of brief, single-informant screening procedures. Focusing on teacher ratings in the school context provides a highly efficient and cost-effective method of identifying early behavior problems, as compared with gathering information from parents or peers, as a teacher is able to rate an entire classroom in one sitting (Petras et al., 2005). Furthermore, teacher ratings of aggressive and disruptive child behavior in first grade predicted difficulties in classroom behavior, academic achievement, and social adjustment in third grade (Flanagan, Bierman, Kam, & CPPRG, 2003). Additionally, teacher nominations were more accurate than parent nominations in predicting which children would develop behavior problems 1 year later (Dwyer, Nicholson, & Battistutta, 2006). This research suggests that teachers may be an ideal source for identifying children who are likely to show later behavior problems and may therefore benefit from participation in preventive interventions. Additionally, several large-scale intervention programs use teacher ratings as the first step in a multiple-gating assessment procedure (e.g., the Fast Track project; Lochman & CPPRG, 1995). Parents of children identified as higher risk based on these teacher ratings can then be contacted to provide their own ratings of their children’s behavior. Given the wide use of teacher ratings, it is important to determine the accuracy and predictive utility of these screening measures.
The Teacher Observation of Classroom Adaptation–Revised (TOCA-R; Werthamer-Larsson, Kellam, & Wheeler, 1991) is a commonly used teacher rating screening tool. The Authority Acceptance (AA) scale of this measure includes 10 items asking teachers to rate the frequency of their students’ behavior problems in the classroom. Scores on the TOCA-R collected during first through fifth grade have been shown to predict later (through age 18) violent and antisocial behavior in both boys and girls as identified in court records (Petras et al., 2004, 2005). In a recent study (Bradshaw, Schaeffer, Petras, & Ialongo, 2010), children were classified into one of three early starter aggressive-disruptive behavior trajectory groups on the basis of their TOCA-R scores during first through fifth grades: chronic high, low-moderate (for girls), or increasing (for boys). As compared with children who did not display behavior problems in elementary school, children in these early starter trajectory groups were found to be at risk for a greater number of negative nonaggressive life outcomes at age 19–20, including early pregnancy and unemployment (for girls) and high school dropout (for boys). Thus, the TOCA-R has demonstrated predictive validity to later problematic outcomes when examined across multiple assessment periods, but its utility as a screener at a single time point early in children’s development (e.g., prior to first grade) is less well established.
Only a few studies have examined the ability of screening measures like the TOCA-R to predict behavior beyond elementary school (cf. Bradshaw et al., 2010; Petras et al., 2004, 2005). Additionally, many of these studies have only examined outcomes in one particular domain or context (e.g., violent behavior, behavior in the classroom, nonaggressive life outcomes) and at one particular point in time. Thus, it is unknown how well early teacher ratings of aggressive and disruptive behaviors, assessed at a single time point, predict behaviors both within and external to the school context, and how these predictions may extend across different developmental periods. Furthermore, it remains unclear how broadly or narrowly this screening measure could be used. For instance, it could be that this measure is a broad predictor, capturing multiple outcomes (e.g., clinical diagnoses of externalizing disorders, substance use, school problems). Conversely, it could be that this measure is a narrow predictor, only addressing school outcomes. As a result of this uncertainty, the utility of this measure to identify children who would benefit from particular prevention programs is still unknown.
Goal of the Current StudyPrevious studies examining the predictive validity of early screening measures of behavior problems such as the TOCA-R have been limited in that they have mostly examined outcomes in one domain or one context (e.g., behavior problems at school), and at one point in time (e.g., third grade). Few studies have examined how well ratings of early behavior problems collected at a single time point predict later negative outcomes and adjustment problems across multiple domains and across distinct developmental periods (i.e., late childhood, middle adolescence, and late adolescence). By examining a variety of outcomes across several developmental periods, it is possible to suggest more targeted interventions for at-risk children. For instance, it may be that high scores on the TOCA-R in kindergarten would predict more school-based behavior problems during late childhood and more delinquency and substance use problems during adolescence. These findings might suggest that for children identified as at risk based on TOCA-R ratings in kindergarten, school-based interventions addressing behavioral and emotional regulation in the classroom would be needed during childhood, whereas more community-based interventions addressing delinquency and substance use would be appropriate during adolescence. To date, no studies using early screening measures have addressed this issue regarding the timing and type of interventions indicated for at-risk children.
Therefore, the goal of the current study was to examine the ability of the TOCA-R measured during kindergarten to predict outcomes at the end of elementary (sixth grade), middle (eighth or ninth grade), and high (11th or 12th grade) school in the following domains: (a) school (behavior problems, social competence, and academic and disciplinary difficulties); (b) clinical diagnoses of externalizing disorders (i.e., attention-deficit/ hyperactivity disorder [ADHD], conduct disorder [CD], and oppositional defiant disorder [ODD]); and (c) substance use (at the end of middle and high school only). In sum, the goal of the current study was to comprehensively examine the prediction of multiple outcomes across multiple domains and developmental periods from the TOCA-R, administered at one time point in kindergarten. To date, no studies have provided this comprehensive evaluation of the predictive utility of the TOCA-R.
Method Participants
Fast Track project
Participants came from a community-based sample of children drawn from the Fast Track project, a longitudinal multisite investigation of the development and prevention of childhood conduct problems (CPPRG, 1992, 2000). Schools within four sites (Durham, NC; Nashville, TN; Seattle, WA; and rural Pennsylvania) were identified as high risk based on crime and poverty statistics of the neighborhoods that they served. Within each site, schools were divided into sets matched for demographics (size, percentage free or reduced lunch, ethnic composition), and the sets were randomly assigned to control and intervention groups. Using a multiple-gating screening procedure that combined teacher and parent ratings of disruptive behavior, 9,594 kindergarteners across three cohorts (1991–1993) from 55 schools were screened initially for classroom conduct problems by teachers using the AA score of the TOCA-R (Werthamer-Larsson et al., 1991; see also Lochman & CPPRG, 1995, for more details regarding screening procedures). The AA scale of the TOCA-R includes 10 items asking teachers to rate the frequency of their students’ behavior problems in the classroom. Those children scoring in the top 40% on the TOCA-R within cohort and site were then solicited for the next stage of screening for home behavior problems by their parents, using items from the Child Behavior Checklist (Achenbach, 199la) and similar scales, and 91% agreed to participate (n = 3,274). The teacher and parent screening scores were then standardized and summed to yield a total severity-of-risk screen score. Children were selected for inclusion into the high-risk sample on the basis of this screen score, moving from the highest score downward until desired sample sizes were reached within sites, cohorts, and groups. Deviations were made when a child failed to matriculate in the first grade at a core school (n = 59) or refused to participate (n = 75) or to accommodate a rule that no child would be the only girl in an intervention group. The outcome was that 891 children (control = 446, intervention = 445) participated.
In addition to the high-risk sample of 891 children, a stratified normative sample of 387 children was identified to represent the population-normative range of risk scores and was followed over time. This normative sample was selected from the control schools, such that 100 kindergarten children were selected at each site (except for Seattle, WA, where only 87 children were selected). Participants in the normative sample were stratified to represent the population according to race, gender, and level of teacher-reported behavior problems (10 children at each decile of the distribution of scores from the TOCA-R). The normative sample included a portion of high-risk control group children to the proportional degree that they were represented in the school population. Written consent from parents and verbal assent from children were obtained. Parents were paid $75 for completing the summer interviews, and teachers were compensated $10 per child for completing the measures. The Institutional Review Boards of the participating universities approved all study procedures.
Sample description
The current study used data from the high-risk control and normative groups. Participants from the high-risk intervention sample were not included in this study. Because 79 of those recruited for the high-risk control group were also included as part of the normative sample, the total sample included 754 participants. However, two children in this sample were missing TOCA-R scores and were therefore excluded from the current analyses, yielding a final sample of 752 children. Children were, on average, 6.55 years old (SD = .43) at the start of the Fast Track project. As would be expected given the higher prevalence of conduct problems documented among boys as compared with girls (Hinshaw & Lee, 2003), 57.7% of the sample was male. Reflecting the ethnic diversity in the populations at the four sites, the majority of the sample was either Caucasian (49.9%) or African American (46.3%), with 3.8% of the sample representing other ethnic groups (e.g., Hispanic, Asian). Due to the multisite sampling design of the Fast Track project, race and urban/rural status were confounded, as nearly all of the African American participants lived in urban areas. In fact, less than 1% of the entire sample consisted of African Americans living in rural communities. Thus, for the current study, a race/urban status variable was examined representing three groups: urban African Americans (46.0%) urban Caucasians (24.2%), and rural Caucasians (25.7%). For analyses examining ethnicity and race/urban status, other ethnic minorities were not included due to the small sample sizes in these groups. For this final sample of 752 children, 147 teachers provided TOCA-R ratings in kindergarten.
Procedure
Annual home interviews were conducted with primary caregivers (typically mothers) and children. Interviews began during the summer before children’s entry to first grade and concluded 2 years after the child completed (or would have completed) 12th grade. Caregivers and children completed the interviews separately with two different interviewers over the course of approximately 2 hr. Measures given during these interviews assessed several domains, including parenting behaviors, child behavior problems, family functioning, parent–child relationship quality, peer relationships, academic achievement, and characteristics of the broader neighborhood. Measures included in the current study are described below.
We chose the specific grades included in this study because they aligned with assessments from the Fast Track project that fell within the developmental periods we aimed to address. The timing of assessments during the Fast Track project also took into account the length of the assessment battery at each year. For instance, in years when the lengthy Computerized Diagnostic Interview Schedule for Children (CDISC; Shaffer & Fisher, 1997) was administered, the remainder of the assessment battery was trimmed significantly to reduce participant burden. For this reason, not all measures were administered at all years, leading to the uneven timing of assessments.
Measures
The TOCA-R
The AA scale of the TOCA-R (Werthamer-Larsson et al., 1991) assessed during kindergarten was used as the predictor in all analyses. The entire TOCA-R is a 43-item questionnaire designed for teachers to assess authority and acceptance behavior, concentration problems, and shy behavior relevant to the child’s behavior in a classroom situation on a 6-point Likert-type scale (0 = never, 1 = rarely, 2 = sometimes, 3 = often, 4 = very often, and 5 = always). The AA scale includes 10 items representing aggressive and disruptive behavior problems (e.g., “breaks rules,” “harms others,” and “takes others’ property”). Scores on the TOCA-R are commonly summed to create a composite sum score (e.g., Petras et al., 2004, 2005). Therefore, for the current study, sum scores were created on the basis of the 10-item AA scale of the TOCA-R (hereafter referred to as TOCA-R sum scores).
School outcomes
Several school-related outcomes were measured at sixth, eighth, and 11th grades, including behavior problems in school, social competence, and academic and disciplinary difficulties. Teacher-rated child behavior problems in school were measured in sixth and eighth grade with the T-score of the Externalizing subscale of the Teacher’s Report Form (TRF; Achenbach, 1991b). The Externalizing subscale of the TRF is a 34-item measure that asked teachers to report on the child’s level of multiple problematic behaviors (e.g., disobedient, disruptive, physically aggressive, explosive, stubborn, truant). Items were scored on a 3-point Likert scale, ranging from 0 (not true [as far as you know]) to 2 (very true or often true). Cronbach’s alpha coefficients for this subscale were .96 in sixth grade and .97 in eighth grade, indicating strong internal consistency.
Teacher-rated social competence in sixth and eighth grade was measured with the Teacher Social Competence (TSC) scale, which was developed by the Fast Track project (CPPRG, 1995). The TSC is a 17-item measure that assessed child competence in academic behavior, prosocial skills, and emotional regulation (e.g., performs academically at grade level, handles disagreements in a positive way, cooperates with others, initiates interactions in a positive manner, recognizes and labels feelings, stops and calms down when excited). Items on this measure asked teachers to rate the frequency of these social behaviors on a 6-point scale, ranging from 0 (almost never) to 5 (almost always). The total score on the TSC was calculated as the mean of all 17 items. Internal consistency of the TSC was strong (Cronbach’s α = .94 in sixth grade and .95 in eighth grade).
Parent- and child-reported academic and disciplinary difficulties in sixth, eighth, and 11th grade were measured with the School Adjustment scale, which was developed by the Fast Track project (CPPRG, 1997a, 1997b). The parent report version of this measure included 18 items that evaluated the child’s past school year in terms of academic performance, disciplinary problems, and general worries about school (e.g., school year difficult for child, school work was really hard for child, other kids tried to make child do bad things, child got into trouble by breaking rules). Two items related to the parent’s contact with the school and teachers were not included in this study. This measure used a 5-point response scale, ranging from 1 (strongly disagree) to 5 (strongly agree). The total academic and disciplinary difficulties-parent report score was calculated as the mean of all 16 items. Cronbach’s alpha coefficients were .90 in sixth and eighth grades and .84 in 11th grade, indicating adequate internal consistency.
The child report version of the School Adjustment scale included 20 items related to the child’s academic and disciplinary difficulties, relationships with other students, and general aspects about the school and teachers. The subscale pertaining to academic and disciplinary difficulties was used for the current study. Example items on this subscale included “the school year was difficult,” “school work was really hard,” “I got into trouble this year,” and “teachers were on me because I broke rules.” The eight items on this subscale were on a 5-point scale ranging from 1 (never true) to 5 (always true). The total academic and disciplinary difficulties-child report score was calculated as the mean of all eight items. Internal consistency of this subscale was adequate (Cronbach’s α = .75 in sixth grade, .76 in eighth grade, and .74 in 11th grade).
Externalizing disorders
The externalizing diagnoses considered for this study included ADHD (combined type), ODD, and CD. Diagnoses of externalizing disorders in sixth, ninth, and 12th grade were assessed with the CDISC (Shaffer & Fisher, 1997). The CDISC is widely used to assess Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, psychiatric symptoms and diagnoses in children and adolescents aged 6–17 years. We analyzed both binary diagnosis variables (i.e., child met diagnostic criteria for a particular disorder or not) and symptom count variables (i.e., a count of how many symptoms of a particular disorder were endorsed). Both parent- and child report versions of this measure were used in the current study, such that a child was considered to have displayed a particular symptom or received a diagnosis in the past year if either the parent or the child or both endorsed that symptom or if the report of the parent or the child or both met criteria for a diagnosis. This approach was used because previous research has shown that parents and children provide unique information regarding ADHD, ODD, and CD diagnostic criteria (Colins, Vermeiren, Schutyen, Broekaert, & Soyez, 2008). For analyses, we examined both diagnoses and symptom counts for each disorder for each grade separately and combined (i.e., a diagnosis of ADHD, ODD, CD, or any externalizing disorder at sixth, ninth, and 12th grade; total symptom counts of ADHD, ODD, CD, or any externalizing disorder at sixth, ninth, and 12th grade).
Substance use
Information regarding children’s substance use was collected with the Tobacco, Alcohol and Drugs measure. This measure was adapted by the Fast Track project from the substance use section of the Self-Administered Youth Questionnaire from the National Longitudinal Survey of Youth 1997 (Bureau of Labor Statistics, 2002; Elliot, Huizinga, & Ageton, 1985). We examined child report of tobacco, alcohol, and marijuana use at eighth and 12th grade. Use of other illicit drugs was not included due to extremely low endorsement of these substances in the sample. For analyses, we examined the number of days children reported smoking cigarettes in the past month (range = 0–30), the number of days they reported drinking alcohol in the past year (range = 0–365), and the number of times children used marijuana in the past month (range = 0–100). Additionally, we examined the number of drinks children reported drinking each time they drank alcohol as well as the number of days children engaged in binge drinking (had 5+ drinks in a row) in the past year (range = 0–365). We also multiplied the number of days children drank alcohol with the number of drinks children had each time they drank alcohol to obtain a measure of intensity of alcohol use. All substance use variables were square-root transformed for analyses due to nonnormality and skewness in the data (Tabachnick & Fidell, 2001).
Missing data
The current analyses examined school outcomes measured at the end of sixth, eighth, and 11th grades; externalizing diagnoses measured at the end of sixth, ninth, and 12th grades; and substance use measured at the end of eighth and 12th grades. For school outcomes, 228 (30.3%) children were missing data in sixth grade, 259 (34.4%) in eighth grade, and 282 (37.5%) in 11th grade. For externalizing diagnoses, 119 (15.8%) were missing data in sixth grade, 185 (24.6%) in ninth grade, and 245 (32.6%) in 12th grade. For substance use, 179 (23.8%) were missing data in eighth grade and 205 (27.3%) in 12th grade. Children with missing data on school outcomes were more likely to be in the high-risk control group than in the normative group: sixth grade, χ2(1, N = 752) = 6.16, p < .05; eighth grade, χ2(1, N = 752) = 6.30, p < .05; 11th grade, χ2(1, N = 752) = 6.39, p < .05. For externalizing diagnoses, children with missing data in 12th grade were more likely to be urban African American, χ2(2, N = 752) = 9.39, p < .01. Additionally, for substance use, children with missing data in 12th grade were more likely to be in the high-risk control group, χ2(1, N = 752) = 16.08, p < .001; urban African American, χ2(2, N = 752) = 10.43, p < .01; and male, χ2(1, N = 752) = 3.94, p < .05. There were no other significant differences in attrition by group (high-risk control vs. normative), race/urban status, gender, or TOCA-R sum score at any of the other time points.
Analysis Plan
Analyses were conducted in SPSS version 14.0 (for descriptive statistics) and Mplus version 6.0 (Muthén & Muthén, 2010). To account for the oversampling of high-risk children in the Fast Track project and to increase generalizability to the population, we used a probability weight based on group (normative vs. high-risk control) that had been previously calculated for all normative and high-risk control group participants (see Jones et al., 2002, for a description of the creation and calculation of this weighting variable). Gender, race/urban status, and risk group (normative vs. high-risk control) were included as covariates in all regression analyses. The research design of the Fast Track project involved children who were nested within classrooms. For example, the 891 high-risk children recruited for this project were nested within 401 first-grade classrooms. However, by Grade 3, these same children were nested in 527 classrooms due to transfers and relocations, and some intervention and control children were in the same classrooms at that time. Therefore, we determined that it was not appropriate or possible to account for nesting within classrooms or schools in the outcome data examined for this study (see CPPRG, 2002, for further details on the nested structure of the data in the Fast Track project).
A series of linear regression analyses were conducted to test the prediction of school outcomes (behavior problems, social competence, and adjustment) from the TOCA-R sum scores. Covariates and TOCA-R sum scores were entered simultaneously as predictors of the various outcomes. Full information maximum likelihood was used to handle missing data (amount of missing data ranged from 15.8% to 37.5% across the variables). We used a maximum likelihood estimator that calculated robust standard errors, which provides valid standard error estimates when variables are nonnormal (Asparouhov & Muthén, 2006). For the prediction of externalizing diagnoses (i.e., ADHD, ODD, and CD), we conducted a series of logistic regressions for the binary diagnostic variables as well as linear regression analyses for the continuous symptom count variables. To predict substance use outcomes, we used a series of censored linear regressions. We used censored regression because the distributions of the substance use variables were continuous, positively skewed with an abundance of zeroes, and theoretically left-censored at zero. Censored regressions are used commonly with variables representing behaviors that do not occur frequently in the general population (e.g., substance use among children, serious delinquency), as data transformations that attempt to normalize the distribution of variables are ineffective in managing an abundance of zeros (Long, 1997). The proportion of variance explained (R2) was used as a measure of the effect sizes for the TOCA-R sum scores in the prediction of outcomes (Cohen, Cohen, West, & Aiken, 2003).
Results Descriptive Statistics
Table 1 provides sample sizes, means, and standard deviations for all continuous outcomes for each year measured. For the binary diagnosis outcomes, the percentage and number of children receiving that diagnosis are presented for each year measured. The zero-order correlations between the TOCA-R sum scores and the school outcomes and between the TOCA-R sum scores and externalizing disorder outcomes are presented in Tables 2 and 3, respectively. As seen in these tables, the TOCA-R sum scores were significantly correlated with almost all outcomes at all time points. Specifically, higher TOCA-R sum scores were associated with more teacher-reported behavior problems, lower teacher-reported social competence, and lower parent- and child-reported school adjustment. In terms of externalizing diagnoses, higher TOCA-R sum scores were related to higher diagnosis rates (with the exception of diagnoses of CD in 12th grade) and symptom counts across all disorders.
Descriptive Statistics of Outcome Variables Measured at the End of Elementary, Middle, and High School
Intercorrelations Between TOCA-R and School Outcomes Measured at the End of Elementary, Middle, and High School
Intercorrelations Between TOCA-R and Externalizing Disorders Measured at the End of Elementary, Middle, and High School
Covariates
Many of the covariates included in the regression models were significant predictors of several of the outcomes included in the current study. The following is a summary of the most consistent covariate effects observed in the analyses (the full results are available from the first author). For teacher-rated school outcomes, children living in urban areas (urban Caucasians and urban African Americans) were rated as higher on behavior problems as compared with rural Caucasians (β = .19, p < .001; β = .30, p < .001, for sixth and eighth grade, respectively) as were urban African Americans when compared with urban Caucasians (βs = −.22, ps < .001 for both sixth and eighth grade). Additionally, children living in urban areas were rated as lower on social competence as compared with rural Caucasians (β = −.18, p < .001; β = −.25, p < .001, for sixth and eighth grade, respectively), as were urban African Americans when compared with urban Caucasians (β = .28, p < .001; β = .26, p < .001, for sixth and eighth grade, respectively). For child-rated school outcomes, children living in urban areas were lower on school adjustment than rural Caucasians (β = −.15, p < .01; β = −.21, p < .001; β = −.15, p < .05, for sixth, eighth, and 11th grade, respectively), and females were higher on school adjustment than males (β = .20, p < .001; β = .18, p < .01; β = .21, p < .01, for sixth, eighth, and 11th grade, respectively).
Males were also consistently higher on symptom counts of ADHD and CD as compared with females (βs range from −.12 to −.30, all ps < .05). Additionally, children living in urban areas had higher symptom counts of CD than rural Caucasians (β = .18, p < .001; β = .21, p < .001; β = .11, p < .05, for sixth, ninth, and 12th grade, respectively), as did urban African Americans when compared with urban Caucasians (β = −.11, p < .05; β = −.12, p < .05; β = −.10, p < .05, for sixth, ninth, and 12th grade, respectively). For substance use in 12th grade, males reported higher levels of use (except for number of drinks consumed each time they drank alcohol, where there was no gender difference) than females (βs range from −.14 to −.26, all ps < .05).
School Outcomes
Table 4 presents the results from the linear regression analyses predicting school outcomes at the end of elementary, middle, and high school from the TOCA-R sum scores. Above and beyond the covariates, teacher-reported behavior problems during kindergarten were prospectively associated with almost all teacher-, parent- and child-rated school outcomes at sixth, eighth, and 11th grades; however, child-rated school adjustment in eighth and 11th grades were not significantly associated. For example, a one standard deviation increase in the TOCA-R sum score predicted a 0.32-standard deviaion increase in teacher-rated behavior problems in sixth grade and a 0.29-standard deviation unit increase in teacher-rated behavior problems in eighth grade. As seen in Table 4, higher kindergarten TOCA-R sum scores predicted higher levels of teacher-rated child behavior problems and lower teacher-rated child social competence at sixth and eighth grades. Higher TOCA-R sum scores also predicted lower parent- and child-rated school adjustment at sixth, eighth, and 11th grades (however, the school adjustment coefficients at eighth and 11th grades were significant according to parent report only). Across reporters and time points, early teacher ratings of behavior problems using the TOCA-R explained approximately 2%–5% of the variance in later behavior problems at school, social competence, and school adjustment, after controlling for the covariates.
Linear Regression Analyses of TOCA-R Sum Scores Predicting School Outcomes
Diagnosis of Externalizing Disorders (ADHD, ODD, and CD)
Of the externalizing disorders, the TOCA-R sum scores predicted only diagnosis of ADHD in ninth grade (β = .30, SE = .12, p < .01, OR = 1.09, 95% CI for OR [1.02, 1.16]). In other words, the odds of receiving an ADHD diagnosis in ninth grade increased between 1.02 and 1.16 times for every one-unit increase in the TOCA-R sum score. The TOCA-R sum scores did not predict any other diagnosis at any grade, nor did they predict any cumulative diagnosis of ADHD, CD, ODD, or any externalizing disorder (ADHD, CD, and ODD combined).
As expected, utilizing symptom counts, compared with predicting dichotomous diagnoses, provided better predictive utility (MacCallum, Zhang, Preacher, & Rucker, 2002). As seen in Table 5, after controlling for the covariates, TOCA-R sum scores in kindergarten prospectively predicted the number of ADHD symptoms endorsed by the parent or the child or both in sixth grade, such that a one-standard deviation increase in TOCA-R sum scores predicted a 0.14-standard deviation unit increase in ADHD symptoms in sixth grade. Additionally, a one-standard deviation increase in TOCA-R sum scores predicted a 0.14-standard deviation unit increase in ninth grade CD symptoms, a 0.12-standard deviation unit increase in total ADHD symptoms (sixth, ninth, and 12th grades combined), and a 0.12-standard deviation unit increase in total externalizing symptoms (ADHD, CD, and ODD combined) across all three grades combined.
Linear Regression Analyses of TOCA-R Sum Scores Predicting Externalizing Disorder Symptom Counts
Substance Use
Results from the censored linear regressions of the substance use variables indicated that, after controlling for the covariates, TOCA-R sum scores in kindergarten were unrelated to any of the substance use outcomes at either eighth or 12th grade (all regression coefficients were not significant at the .05 level), with the exception of cigarette use in 12th grade. This finding indicated that higher TOCA-R sum scores predicted more days smoked cigarettes in the past month (β = .20, p < .05, 95% CI [.03, .36]).
Replication of Findings
It is important to note that sum scores, as reflective of classical test theory, provide equal weight to each behavior on a particular screening measure. Therefore, sum scores may not accurately capture the true severity of a child’s aggressive and disruptive behaviors, as some behaviors (e.g., being stubborn and disobedient) are considered less severe than others (e.g., fighting and harming others). Several researchers have argued that item response theory (IRT), which explicitly assesses differential severity across items (and thus behaviors), may be a more appropriate method for modeling the items used in brief screening scales for behavior problems (Embretson & Reise, 2000). In the current study, the findings from the TOCA-R sum scores were replicated with IRT-based TOCA-R scores (see Wu et al., 2012, for a description of the creation of the IRT TOCA-R scores). The IRT scores demonstrated a similar pattern of prediction to later school outcomes, externalizing diagnoses and symptom counts, and substance use outcomes as seen with the TOCA-R sum scores. Furthermore, the standard errors and confidence intervals were relatively similar between these two sets of analyses. However, the IRT TOCA-R scores tended to account for slightly more variance in the majority of the outcome variables as compared with the TOCA-R sum scores.
We also ran post hoc regression analyses without controlling for the effects of the covariates. A similar pattern of findings was observed in these analyses, as higher TOCA-R sum scores predicted more behavior problems at school, lower social skills, and poorer school adjustment. Additionally, in these analyses higher TOCA-R sum scores predicted more cigarette use in eighth and 12th grades. Higher TOCA-R sum scores also predicted higher externalizing disorder symptom counts (for ADHD, CD, and ODD) as well as higher odds of receiving an externalizing diagnosis. Lastly, although it was not possible for us to account for nesting within classrooms or schools in the outcome data examined for this study, we conducted post hoc regression analyses accounting for clustering in the TOCA-R sum scores collected during kindergarten (clusters were based on classrooms). A similar pattern of findings was observed in these analyses as was seen in the initial regression analyses, with similar beta coefficients obtained for all outcomes.
DiscussionThe goal of the current study was to examine the predictive utility of the TOCA-R (specifically, the AA scale), a commonly used teacher screening measure, administered during kindergarten. We extended prior research by examining the TOCA-R at a single time point at an earlier age than examined in previous studies. Additionally, we examined a broader range of outcomes across a broader range of developmental periods than has been documented in the extant literature. Our results suggested that higher TOCA-R kindergarten scores were associated with more behavior problems at school, lower social skills, and poorer school adjustment reported by multiple informants (teacher, parent, and child) at the end of elementary, middle, and high school. The TOCA-R sum scores were also related, although somewhat more inconsistently, to the odds of an ADHD diagnosis, as well as ADHD, CD, and externalizing disorder symptom counts, but not to an ODD diagnosis or symptoms or any substance use outcomes.
The findings from the current study indicated that early teacher ratings consistently predicted later school outcomes, including school adjustment and social competence, as late as the end of high school. The prediction of ADHD diagnosis in ninth grade and symptoms in the sixth and ninth grades further supports the ability of the TOCA-R to predict outcomes mainly within the school setting, as teachers frequently play an important role in initial screenings for ADHD symptoms (Snider, Busch, & Arrowood, 2003; Snider, Frankenberger, & Aspenson, 2000). Additionally, ADHD symptoms are frequently first observed and most troublesome in the classroom where children are required to sustain their attention and refrain from hyperactive or impulsive behaviors (Barkley, 2003). Overall, these findings suggest that the TOCA-R may be most useful when predicting problematic behaviors that occur within the classroom.
A strength of the current study is the inclusion of a racially and regionally diverse sample. Prior research has demonstrated that the TOCA-R administered in kindergarten exhibits group differences, as boys and African Americans have a higher overall mean, and therefore more frequent behavior problems, than girls or Caucasians, respectively (Koth, Bradshaw, & Leaf, 2009). In previous work with the same larger sample as the current study (Wu et al., 2012), item bias by gender was revealed in the TOCA-R, such that at equivalent levels of latent behavior problems, males received more endorsements of overt behaviors (e.g., harming others, fights, breaks things) from teachers, whereas females received more endorsements of nonphysical behaviors (e.g., stubborn, takes property, lies). Moreover, overt behaviors tended to be better at distinguishing among levels of latent behavior problems for males, and covert behaviors tended to be better at distinguishing among levels of latent behavior problems for females. However, given the similarity in the results obtained from the TOCA-R sum scores and the IRT scores, our findings suggest that the gender bias identified by Wu and colleagues (2012) may not affect the predictive utility of this screening measure.
The importance of including diverse samples in studies examining problem behaviors is underscored by the significant covariate effects in the current study. For instance, children living in urban areas were found to be rated as higher in behavior problems, lower in social competence and school adjustment, and higher in endorsed symptoms of CD than children in rural areas. Compared with urban Caucasians, urban African Americans also received the lowest/highest scores, indicating that this population is particularly at risk for the development of these problematic behaviors. Additionally, when compared with females, males were found to be rated as lower in school adjustment and higher in endorsed symptoms of ADHD and CD. These findings are consistent with other studies indicating that males, ethnic minorities, and children living in urban areas are at most risk for behavior problems (Hinshaw & Lee, 2003). It is therefore important that future studies continue to incorporate diverse samples (i.e., in terms of race, gender, and geographical location). Overall, the findings from the current study suggest that in applied settings, such as schools or community mental health centers, or when a quick screening of behavioral problems is needed, the calculation of a sum score on the TOCA-R may be sufficient to determine which children are at risk for later problems.
The post hoc regression analyses without controlling for the effects of the covariates allowed for exploration of the utility of the TOCA-R in applied settings, as it is unlikely that professionals in these settings would have the resources or information needed to consider these demographic factors in their screening procedures. A similar pattern of findings was observed in these analyses, providing support for the utility of the TOCA-R as a kindergarten screener in practical, applied settings (e.g., schools, community mental health centers, private practice offices). However, it is important to note that the findings regarding diagnostic outcomes were not significant in the regression analyses with covariates, suggesting caution in using the TOCA-R and similar brief screening measures to predict whether a child will receive a diagnosis. Such diagnostic decisions require careful, evidence-based assessments (Mash & Hunsley, 2005) along with the inclusion of demographic variables known to account for a significant amount of variance in these disorders (e.g., race, gender, etc.).
Clinical Implications and Future Directions
Generally, studies examining the development of maladaptive and problematic behaviors have focused mainly on elementary school, with little attention to earlier behaviors during preschool and kindergarten (Campbell et al., 2000). The current study shows that early behavior problems identified in kindergarten with the TOCA-R can reliably predict a range of adverse school-related outcomes in late childhood, middle adolescence, and late adolescence. Therefore, the TOCA-R may be most effective in identifying which children will benefit from prevention programs targeting problems within the school environment and across the school years (e.g., elementary, middle, and high school). It may be that classroom-based intervention programs would be best suited to implement with children at high levels of TOCA-R sum scores. Several existing interventions target improvements in school behavior. Examples include Promoting Alternative Thinking Strategies (Kusche & Greenberg, 1994), Coping Power (Lochman & Wells, 2003), and the Good Behavior Game (Barrish, Saunders, & Montrose, 1969).
The identification of children at risk for a range of negative outcomes beyond the school context may require the application of additional, broader screening measures. Specifically, other screening measures may be needed to identify children at risk for later outcomes that occur in contexts outside of the school setting (i.e., diagnoses of externalizing disorders and substance use), which may require information from additional reporters (Hill et al., 2004; Kerr, Lunkenheimer, & Olson, 2007; Lochman & CPPRG, 1995). Cost–benefit analyses are needed to determine whether adding additional raters is beneficial and outweighs the added cost of both time and resources needed to collect these ratings. Moreover, given previous research demonstrating that the TOCA-R did not adequately cover the lower range of problem behaviors (Wu et al., 2012), it may be that adding additional items to the TOCA-R may improve specificity of prediction to later ages. For example, recent research has suggested that poor self-control observed as early as age 3 predicts substance use disorders at age 32 (Moffitt et al., 2011). Improving prediction to later ages may involve creating measures that blend traditional symptom checklists with measures that tap the underlying processes (e.g., poor self-control) that increase risk for those symptoms. Future research should continue to explore the optimal combination of screening measures to capture the full range of negative outcomes that could be experienced by children and adolescents.
The findings from the current study indicate that early behavior problems that place children at risk for later adverse school-related outcomes can be identified as early as kindergarten. The results from this study therefore suggest that kindergarten may be an appropriate time to begin delivering prevention programs that address aggressive and disruptive behaviors in children. The prompt application of effective prevention programs with these children may interrupt the progression of behavior problems in school before they become entrenched and difficult to change (Lochman & CPPRG, 1995). However, the ability of these programs to effectively address these negative behaviors relies on the accurate identification of children who need these preventive interventions (Hill et al., 2004; O’Connell, Boat, & Warner, 2009). Future studies should therefore continue to explore the predictive validity of methods used in the early identification of at-risk children (Keenan & Wakschlag, 2002; Wakschlag & Keenan, 2001).
The timing of screening measures is an important consideration, and the administration of these measures may need to be altered depending on the purpose of the screening. For instance, previous studies have suggested that prediction to later outcomes is enhanced when TOCA-R ratings collected during first grade are used (Flanagan et al., 2003; Hill et al., 2004). Petras and colleagues (2004,2005) have reported that the spring of third grade for boys and fifth grade for girls are the optimal times for minimizing both false-negative and false-positive identifications of children at risk for later problems based on TOCA-R sum scores. The findings from the current study suggest that for the general identification of children at risk for a variety of school-based negative outcomes at various developmental stages, administration of the TOCA-R during kindergarten is warranted. Future research should seek to refine conclusions regarding the ideal timing of TOCA-R administration.
Limitations
Limitations of the current study should be noted. First, the early behavior ratings examined in the current study only described a minimal amount of the variance (2%–5%) in later teacher-rated behavior problems, social competence, and school adjustment. More research is needed to identify additional factors that may explain more of the variance in these behavior and adjustment difficulties (e.g., parenting, peer engagement in antisocial behaviors, environmental context). Additionally, although the current study is longitudinal, care should be taken to not assume causal relationships between TOCA-R sum scores and outcomes, as several third variables may influence this relationship (e.g., low or unchanging teacher expectations, negative perceptions of children’s classroom behavior, and academic achievement).
ConclusionIn summary, this study illustrates the predictive validity of TOCA-R sum scores when predicting various outcomes, particularly those observed within the school setting, across a range of developmental periods. The ability of the TOCA-R to predict outcomes into late adolescence speaks to the benefit of using a widely distributed brief teacher report screening instrument. The findings also indicate that children identified as at risk for later behavior problems experience difficulty with school, teachers, and their fellow classmates. Prevention programs working with at-risk children should therefore continue using strategies that promote the development of academic skills, prosocial interactions with peers and teachers, and overall positive attitudes toward school. Taken together, this study, as well as other studies examining teacher-reported screening tools, supports the adage to “catch behavior problems early,” before children are directed into a persistent negative life course trajectory.
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Submitted: October 16, 2011 Revised: September 11, 2012 Accepted: February 12, 2013
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Source: Journal of Consulting and Clinical Psychology. Vol. 81. (4), Aug, 2013 pp. 588-599)
Accession Number: 2013-11001-001
Digital Object Identifier: 10.1037/a0032366
Record: 52- Title:
- The relation between changes in patients' interpersonal impact messages and outcome in treatment for chronic depression.
- Authors:
- Constantino, Michael J.. Department of Psychology, University of Massachusetts, Amherst, MA, US, mconstantino@psych.umass.edu
Laws, Holly B.. Department of Psychology, University of Massachusetts, Amherst, MA, US
Arnow, Bruce A.. Department of Psychiatry and Behavioral Sciences, Stanford University Medical Center, Stanford, CA, US
Klein, Daniel N.. Department of Psychology, State University of New York at Stony Brook, Stony Brook, NY, US
Rothbaum, Barbara O.. Department of Psychiatry, Emory University School of Medicine, Atlanta, GA, US
Manber, Rachel. Department of Psychiatry and Behavioral Sciences, Stanford University Medical Center, Stanford, CA, US - Address:
- Constantino, Michael J., Department of Psychology, University of Massachusetts, 612 Tobin Hall, Amherst, MA, US, 01003-9271, mconstantino@psych.umass.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 80(3), Jun, 2012. pp. 354-364.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- chronic depression, cognitive-behavioral analysis system of psychotherapy (CBASP), impact messages, interpersonal change, treatment outcome, drug therapy, nefazodone, combined therapy
- Abstract:
- Objective: Interpersonal theories posit that chronically depressed individuals have hostile and submissive styles in their social interactions, which may undermine their interpersonal effectiveness and maintain their depression. Recent findings support this theory and also show that patients' interpersonal impact messages, as perceived by their psychotherapists, change in theoretically predicted ways following cognitive-behavioral analysis system of psychotherapy (CBASP) alone or with medication. This study extended these previous findings by examining whether such changes were associated with their depression change and response status. Method: Data derived from a large clinical trial for chronic depression compared the efficacy of CBASP, nefazodone, and their combination. To assess patients' impact messages, CBASP clinicians completed the Impact Message Inventory (IMI; Kiesler & Schmidt, 1993) following an early and late session. Our subsample (N = 259) consisted of patients in the CBASP and combined conditions who had depression severity data for at least 1 post-randomization visit and whose clinicians completed at least 1 IMI rating. We used hierarchical linear modeling (HLM) to calculate IMI change scores and to model depression change. We used HLM and logistic regression to test our predictor questions. Results: As hypothesized, decreases in patients' hostile–submissive impact messages were significantly associated with depression reduction (γ = 0.27, 95% CI [0.11, 0.43], p < .01) and favorable treatment response (B = –0.05, 95% CI [–0.09, –0.01], p = .03), regardless of treatment condition. Conclusions: The findings support CBASP theory, suggesting that interpersonal change is related to depression reduction among chronically depressed patients. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Major Depression; *Multimodal Treatment Approach; *Psychotherapy; *Treatment Outcomes; Cognitive Behavior Therapy; Drug Therapy; Interpersonal Interaction; Interpersonal Psychotherapy; Nefazodone; Treatment Effectiveness Evaluation
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Aged; Antidepressive Agents; Cognitive Therapy; Combined Modality Therapy; Depressive Disorder; Female; Humans; Interpersonal Relations; Male; Middle Aged; Psychiatric Status Rating Scales; Treatment Outcome; Triazoles
- PsycINFO Classification:
- Psychotherapy & Psychotherapeutic Counseling (3310)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs)
Aged (65 yrs & older) - Tests & Measures:
- Hamilton Rating Scale for Depression DOI: 10.1037/t04100-000
Impact Message Inventory DOI: 10.1037/t02262-000
Structured Clinical Interview for DSM-IV Axis I Disorders - Grant Sponsorship:
- Sponsor: Bristol-Myers Squibb
Recipients: No recipient indicated - Conference:
- Annual Meeting of the Society for Psychotherapy Research, 41st, Jun, 2010, Asilomar, CA, US
- Conference Notes:
- A version of this article was presented at the aforementioned conference.
- Methodology:
- Empirical Study; Quantitative Study; Treatment Outcome
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Apr 30, 2012; Accepted: Mar 13, 2012; Revised: Jan 11, 2012; First Submitted: Sep 27, 2010
- Release Date:
- 20120430
- Correction Date:
- 20120827
- Copyright:
- American Psychological Association. 2012
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0028351
- PMID:
- 22545738
- Accession Number:
- 2012-10796-001
- Number of Citations in Source:
- 38
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-10796-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-10796-001&site=ehost-live">The relation between changes in patients' interpersonal impact messages and outcome in treatment for chronic depression.</A>
- Database:
- PsycINFO
The Relation Between Changes in Patients' Interpersonal Impact Messages and Outcome in Treatment for Chronic Depression
By: Michael J. Constantino
Department of Psychology, University of Massachusetts Amherst;
Holly B. Laws
Department of Psychology, University of Massachusetts Amherst
Bruce A. Arnow
Department of Psychiatry and Behavioral Sciences, Stanford University Medical Center
Daniel N. Klein
Department of Psychology, State University of New York at Stony Brook
Barbara O. Rothbaum
Department of Psychiatry, Emory University School of Medicine
Rachel Manber
Department of Psychiatry and Behavioral Sciences, Stanford University Medical Center
Acknowledgement: A version of this article was presented at the 41st annual meeting of the Society for Psychotherapy Research, Asilomar, California, June 2010. This research was supported by Bristol-Myers Squibb. We are grateful to Aline G. Sayer for her statistical guidance.
Major depression is highly prevalent and often recurrent in course (Constantino, Lembke, Fischer, & Arnow, 2006). Chronic forms of depression, in which symptoms persist for 2 years or longer without remission, account for about one third of all episodes of major depression (Kocsis et al., 2003) and affect approximately 3%–5% of the United States' population (Keller & Hanks, 1995a). To a higher degree than acute depression, chronic forms are associated with severe vocational and psychosocial impairment (Cassano, Perugi, Maremmani, & Akiskal, 1990; Wells, Burnam, Rogers, Hays, & Camp, 1992), frequent suicide attempts (Howland, 1993; Klein, Taylor, Harding, & Dickstein, 1988), and remarkably high health care costs (Howland, 1993; Weissman, Leaf, Bruce, & Florio, 1988). However, only recently has chronic depression received heightened conceptual, clinical, and empirical attention (e.g., Cuijpers et al., 2010; Keller & Hanks, 1995b; Keller et al., 2000; Klein & Santiago, 2003; Kocsis et al., 2009).
In a comprehensive, interpersonally focused theory of chronic depression, McCullough (2000) pointed to arrested social development as both a cause and sustaining consequence of chronic depressive symptomatology. In particular, McCullough theorized that chronically depressed individuals function pre-operationally (Piaget, 1926, 1981) when cognitively processing their social interactions. Unable to appraise effectively the consequences of their own behavior or to process accurately feedback and/or cause and effect associations in interpersonal exchanges, chronically depressed individuals (according to McCullough's, 2000, theory) lack the ability to act effectively on their interpersonal environment. Thus, they remain interpersonally unfulfilled and unskilled, as well as emotionally dysphoric. Although interpersonal deficits, generally speaking, are characteristic of all forms of depression (Bifulco, Moran, Ball, & Bernazzani, 2002; Coyne, 1976; Joiner & Timmons, 2009), McCullough has postulated that pre-operational functioning in chronically depressed patients often manifests specifically as hostile detachment and excessive submissiveness to a degree that differentiates chronically from acutely depressed people.
Evolving from his theory, McCullough (2000) developed cognitive-behavioral analysis system of psychotherapy (CBASP) to treat specifically chronic depression. CBASP is an integrative cognitive, behavioral, and interpersonal treatment that aims to enhance patients' understanding of the consequences of their actions, to help patients be more affiliative and connected to their interpersonal environment, and to help patients become more effectively assertive. These tasks are accomplished through three primary strategies. The first, situational analysis (SA), is a multi-step, problem-solving algorithm designed to improve patients' operational thinking by closely analyzing distressing interpersonal experiences. The second, the interpersonal discrimination exercise (IDE), involves the psychotherapist's use of transference hypotheses to help patients process how their current relationship with him or her is different from past relationships, and, thus, the same fears, expectations, and defenses need not apply. Finally, the third strategy, behavioral skill training/rehearsal (BST/R) focuses directly on skill development relevant to social exchange (e.g., assertiveness training, emotion regulation). As reflected in these interventions, especially the IDE, the therapy relationship in CBASP is conceptualized as a central change agent capable of promoting a corrective interpersonal experience.
CBASP, especially in combination with medication, has shown some efficacy in the treatment of chronic depression. In a well-powered (N = 681 patients) multi-center non-inferiority trial comparing CBASP alone, nefazodone alone, and their combination, Keller et al. (2000) reported modified intent-to-treat (ITT) response rates of 48%, 48%, and 73%, respectively (the modified ITT sample included the 656 participants with depression data for at least one post-randomization session). However, in a follow-up multi-center non-inferiority trial examining the influence of adding psychotherapy (Phase 2) to continued pharmacotherapy for nonresponders or partial responders (N = 491) to an initial medication trial (Phase 1) for chronic depression, there were no significant differences in Phase 2 response rates among patients whose continued treatment was augmented with CBASP or brief supportive psychotherapy (BSP), or those who continued optimized pharmacotherapy alone (Kocsis et al., 2009). Counter to predictions, the findings did not support the value of psychotherapy augmentation over pharmacotherapy augmentation/switching alone, nor did they support efficacy value added in CBASP over BSP. Thus, the current efficacy data on CBASP remain mixed, which suggests the need not only for additional efficacy trials but also for process research that might illuminate potential change ingredients that could be highlighted in future refinements of CBASP.
Focusing on the process of change, Constantino et al. (2008) formally tested the interpersonal tenets underlying McCullough's (2000) chronic depression theory and examined whether CBASP promoted interpersonal change in theory-specified ways. These authors first examined interpersonal profiles among the chronically depressed outpatients receiving CBASP in Keller et al.'s (2000) trial, as well as both an acutely depressed outpatient comparison sample receiving interpersonal therapy (IPT; McBride et al., 2010) and a non-clinical comparison sample (Kiesler & Schmidt, 1983). Across these samples, interpersonal styles were assessed from the perspective of an individual interacting with the patients or the non-clinical comparison group participants (i.e., psychotherapists in the two clinical groups, undergraduate subjects viewing non-maladjusted psychiatric interview participants in the non-clinical group) using the Impact Message Inventory (IMI; Kiesler & Schmidt, 1993). The IMI, a self-report measure, is based on the assumption that an individual's interpersonal style can be validly assessed by the interpersonal “impact messages” received by an interactant during communication with the individual (Kiesler, 1996). The IMI items form a circumplex comprising eight scales reflecting combinations of the central interpersonal dimensions of affiliation (ranging from hostility to friendliness on the x-axis) and control (ranging from dominance to submission on the y-axis). The measurement of impact messages is based on the complementarity principle. Theoretically, interpersonal behaviors are complementary if similar in affiliation and opposite in control (Carson, 1969; Kiesler, 1983, 1996). For example, if a psychotherapist endorsed feeling “in charge” when interacting with a patient, it would suggest that the patient's impact message is one of submissiveness—that is, the patient's deference would be evoking complementary dominance in the clinician (dominance is the interpersonal opposite of control). As noted, McCullough's (2000) theory purports that chronically depressed individuals should peak on hostile and submissive impact messages, reflecting the nature of their pathology and their difficulty getting their interpersonal needs met because of their inability to be flexible, affiliative, and effectively assertive (i.e., flexible and friendly–dominant). CBASP psychotherapists are trained to use the IMI to help identify their own objective countertransference (Kiesler, 1996)—that is, responses evoked in their interactions with the patient. Such monitoring can inform potential transferential “hot spots” requiring attention in the IDE as well as SA and BST/R.
Constantino et al.'s (2008) findings mostly supported McCullough's (2000) theory in terms of presenting interpersonal profiles. The chronically depressed patients receiving CBASP in Keller et al.'s (2000) trial presented with more hostile and submissive impact messages than friendly–dominant impact messages (as per their psychotherapists' IMI ratings early in treatment). Furthermore, at this early stage of treatment, chronically depressed patients were rated as having significantly higher hostile and hostile–dominant, and significantly lower friendly and friendly–dominant, impact messages on their psychotherapists than acutely depressed patients had on their psychotherapists at a comparable time in brief IPT. The chronically depressed patients also had higher hostile, hostile–submissive, and hostile–dominant, and significantly lower friendly–dominant, friendly, and friendly–submissive, impact messages on their clinicians than the normative comparison groups' impact messages on a rating other.
Constantino et al. (2008) also examined how chronically depressed patients' IMI profiles changed by the end of CBASP (as the clinicians also completed the IMI during the final week of the 12-week treatment), delivered either alone or with pharmacotherapy. The findings were again consistent with CBASP theory in that patients' impact messages were perceived by their psychotherapists as less hostile, hostile–submissive, and hostile–dominant, and more friendly, friendly–dominant, and friendly–submissive by treatment's end. Importantly, it did not appear that IMI change simply reflected improvement in depression, as change was comparable for patients who received CBASP alone or CBASP with pharmacotherapy despite the greater efficacy (in terms of depression reduction) of the combined treatment group. Furthermore, by the end of treatment, the chronically depressed patients' impact messages were mostly equivalent with those of the two comparison groups. The only exception was friendly–dominant, for which the chronically depressed patients continued to be rated significantly lower than the normative comparison sample.
Although Constantino et al.'s (2008) findings showed promising initial support for the primary interpersonal tenets of McCullough's (2000) chronic depression theory and theory of change in CBASP, it remains unclear if changes in patients' interpersonal impact messages are associated with treatment outcome in the form of depressive symptom reduction. Thus, the primary aim of the current study was to extend Constantino et al.'s findings by examining whether changes in patients' impact messages, as perceived by their psychotherapist, relate to depression change and posttreatment response status in Keller et al.'s (2000) trial. Consistent with McCullough's theory, we hypothesized that (a) a decrease in hostile–submissive impact messages (reflecting more adaptive interpersonal affiliation and balance in self–other reliance) would be associated with greater depression reduction over time and with better posttreatment response, and (b) an increase in friendly–dominant impact messages (reflecting adaptive interpersonal assertiveness) would also be associated with greater depression reduction and better response.
Method Data Set Overview
Data for the current study derived from the acute phase of the aforementioned multi-center (12 sites) randomized clinical trial compared 12 weeks of CBASP, nefazodone, and their combination for chronic depression (Keller et al., 2000). For the trial, 681 adults were randomly assigned to treatment condition. Because no outcome data were collected for dropouts, the primary outcome analyses discussed above were conducted on a modified intent-to-treat sample that included all patients who had at least one efficacy assessment beyond baseline (total N = 656). Patients averaged 43.5 years of age (SD = 10.7 years; range = 18–75 years) and met Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM–IV; American Psychiatric Association, 1994) criteria for a current and principal form of nonpsychotic chronic depression as determined by the Structured Clinical Interview for DSM–IV Axis I Disorders (SCID-I; First, Spitzer, Gibbon, & Williams, 1995). The three eligible depression forms included the following: (a) major depressive disorder (MDD) lasting at least 2 years, (b) recurrent MDD with incomplete interepisode remission and a total continuous duration of at least 2 years, or (c) a major depressive episode superimposed on antecedent dysthymia. Patients also had to receive a score of at least 20 on the 24-item Hamilton Rating Scale for Depression (HRSD; Hamilton, 1967) at screening and at baseline following a 2-week drug-free period. Diagnostic exclusion criteria included the following: a history of bipolar disorder, obsessive-compulsive disorder, or dementia; an eating disorder within the past year; substance abuse or dependence in the past 6 months; antisocial, schizotypal, or severe borderline personality disorder; high suicidal risk; or an unstable medical condition. Patients were also excluded for non-response to at least three previous trials of at least two different classes of antidepressants or electroconvulsive therapy, or to at least two previous courses of empirically supported psychotherapy within the past 3 years. There were no significant differences between the treatment groups with respect to baseline characteristics and clinical characteristics (when analyzed both across and within sites; see Keller et al., 2000, for additional details and descriptive statistics on the total sample).
Across the sites, 52 psychotherapists conducted CBASP. All had several years of experience, attended a 2-day workshop conducted by J. McCullough, and demonstrated mastery of the treatment protocol in their work with two pilot cases. During the study, site supervisors reviewed session videos on a weekly basis to ensure standard protocol administration. In the combined condition, psychopharmacologists prescribed nefazodone.
CBASP, described above, was manual-guided and 12 weeks long. The protocol specified twice-weekly sessions for the initial 4 weeks and weekly sessions thereafter. Twice-weekly sessions could be extended up to Week 8 if the patient did not demonstrate mastery of the primary therapeutic skill (i.e., situational analysis). Thus, session frequency could range from 16 to 20. For the overall modified ITT sample (Keller et al., 2000), the average CBASP session frequency was 16.2 (SD = 4.8) for CBASP alone patients and 16.0 (SD = 4.7) for combined treatment patients.
Pharmacotherapy consisted of open-label nefazodone in two divided doses. The initial dose was 200 mg per day, with a 300 mg per day dose required by Week 3. Subsequent titration of divided doses was allowed up to 600 mg per day until maximum efficacy and tolerability were achieved. For the overall modified ITT sample (Keller et al., 2000), the average final nefazodone dose in the combined group was 460 mg per day (SD = 139 mg per day). Medication management (i.e., 15–20 min visits conducted weekly during the initial 4 weeks and biweekly thereafter) followed a published manual (Fawcett, Epstein, Fiester, Elkin, & Autry, 1987) focused on symptoms, side effects, and promotion of a biochemical rationale for depression response. Psychopharmacologists were not allowed to conduct formal psychotherapeutic interventions. The institutional review boards at each site approved the study protocol, and all participants gave written informed consent before study entry.
Current Subsample
The current subsample is restricted to participants in CBASP and combined treatment, as only CBASP psychotherapists completed the IMI. Of the 438 patients in these two groups who provided at least one post-randomization data point (modified ITT), 179 were excluded from the current analyses because the clinician did not fully complete the IMI measure for at least one of the two assessments. Thus, the final subsample for the current study was 259 patients. The average age of our subsample patients (across both treatment groups) was 44.8 years (SD = 10.1 years), with the majority being female (64.5%), White (93.1%), and neither married nor cohabitating (55.2%). Their average monthly income was $2,187 (SD = $2,464). Diagnostically, 33.2% met criteria for chronic MDD, 23.2% met criteria for recurrent MDD with incomplete interepisode remission, and 43.6% met criteria for MDD superimposed on preexisting dysthymia. The mean baseline HRSD score was 27.7 (SD = 5.2). The mean durations for the current MDD episode and the current dysthymic episode were 9.3 years (SD = 11.2 years) and 24.2 years (SD = 15.5 years), respectively, with average age of onsets of 27.9 (SD = 13.9) and 19.4 (SD = 13.9), respectively. Within our subsample, 40.8% had a non-exclusionary comorbid personality disorder. With respect to baseline demographic and clinical features for our subsample, the only marginally significant difference between CBASP and combined treatment patients was for the diagnosis of a comorbid personality disorder. More patients in the combined group (42.8%) were diagnosed with a personality disorder than in CBASP alone (34.4%), χ2(1) = 3.58, p = .06.
The patients in our subsample were similar to those excluded because of missing IMI data on most of the above sample characteristics. However, several significant differences existed. Patients in our subsample were slightly older (M = 44.8, SD = 10.1) than those excluded (M = 42.8, SD = 10.9), t(436) = 1.94, p = .05. Patients in our subsample also had significantly higher baseline HRSD scores (M = 27.7, SD = 5.2) than those excluded (M = 25.8, SD = 4.6), t(436) = 3.88, p < .01, and had a longer length of current MDD episode (M = 9.3, SD = 11.2) than those excluded (M = 6.3, SD = 7.0), t(436) = 3.18, p < .01. Finally, there were significantly more patients in our subsample with a personality disorder diagnosis (40.5%) than in those excluded from analyses (36.6%), χ2(1) = 8.75, p < .01.
Of the 259 patients in our subsample, 141 had IMI measurements for both Weeks 2 and 12, 111 had IMI measurements at Week 2 only, and 7 had IMI measurements at Week 12 only (we discuss below our method for deriving the relevant IMI change scores).
Measures and Data Collection
Impact Message Inventory (IMI)
Following Session 2 (Week 1) and the final session (Week 12), CBASP psychotherapists completed the octant scale version of the IMI (Kiesler & Schmidt, 1993) to assess their perceptions of their patients' interpersonal impact messages. The IMI consists of 56 items rated on a 4-point scale ranging from 1 (not at all) to 4 (very much so). The measure possesses good internal consistency and quasi-circumplex structure based on the underlying dimensions of affiliation and control (Schmidt, Wagner, & Kiesler, 1999). Each octant, or vector, reflects the sum of 7 items. The present study focused on the two theoretically relevant vectors of hostile–submissive (HS; Week 2 α = .80, Week 12 α = .86) and friendly–dominant (FD; Week 2 α = .76, Week 12 α = .78). All IMI items begin with the phrase, “When I am with this person, he or she makes me feel…” Sample HS items include, “… that I should tell him/her not to be so nervous around me” and “… that he/she thinks he/she can't do anything for him/herself.” Sample FD items include, “… that I could relax and he/she'd take charge” and “… entertained.” For this study, we calculated weighted vector scores based on the geometry of the circle and taking into account information from adjacent vectors. The weighted HS formula is HS + .707 (H + S), and the weighted FD formula is FD + .707 (D + F). The theoretical range for weighted vector scores is 16.90 to 67.59.
Hamilton Rating Scale for Depression (HRSD)
The 24-item HRSD (Hamilton, 1967) was used to assess patient depression at baseline and following treatment Weeks 1, 2, 3, 4, 6, 8, 10, and 12. The HRSD is the most widely used interviewer-administered depression instrument, with a majority of studies reporting adequate internal consistency (α ≥ .70; Bagby, Ryder, Schuller, & Marshall, 2004). Interrater reliability estimates are less consistent, with Bagby et al. (2004) reporting an intraclass r range from .46 to .99. To promote high interrater agreement in Keller et al.'s (2000) trial, all raters went through a strict certification process in HRSD administration. Raters were also blind to treatment condition. The HRSD was used to assess both depression level, as well as treatment response. In Keller et al.'s study, as well as the current analyses, a single positive response group was formed. This dichotomized group included patients who either (a) remitted (i.e., had an HRSD score of no more than 8 at both Weeks 10 and 12 for completers or at the time of withdrawal for noncompleters) or (b) had a satisfactory response (i.e., had at least a 50% reduction in HRSD score from baseline to Weeks 10 and 12, with a total score of 15 or less at these times, but of more than 8 at Week 10, Week 12, or both for study completers or at the time of withdrawal for noncompleters).
Results Preliminary Analyses
To capture change on the relevant HS and FD weighted IMI vectors, we created latent difference scores using hierarchical linear modeling (HLM; Collins & Sayer, 2001; Raudenbush & Bryk, 2002). Specifically, we used the HLM 6 program (Raudenbush, Bryk, & Congdon, 2004) to fit a two-wave model of change to each individual's data and obtained the model-based empirical Bayes estimates of each person's change score for use in the primary analyses. This empirical Bayes estimate of change is a composite that combines information about change from each individual and information from the group as whole, with each part weighted by its reliability. Individuals with one data point provide less reliable evidence for change and therefore change estimates for those with only one IMI measure were weighted toward the group mean change score. This is a standard approach for handling missingness in hierarchical linear models (Raudenbush & Bryk, 2002). Negative scores indicate a decrease in interpersonal characteristics from Week 2 to Week 12, whereas positive scores indicate an increase. Change in HS was significantly different from zero and negative, indicating that, on average, patients' HS impact messages decreased significantly over time (γ = –4.97, p < .001). Change in patients' FD impact messages was significantly different from zero and positive (γ = 3.12, p < .001); on average, patients became significantly more FD by treatment's end.
Given that previous analyses of Keller et al.'s (2000) trial data showed that early and middle patient-rated therapeutic alliance quality were positively associated with posttreatment outcome (Klein et al., 2003), we also examined the association between the early HS vector and alliance (as assessed with the brief version of the Working Alliance Inventory; Tracey & Kokotovic, 1989). We did this to ensure that these are two distinct constructs (as opposed to early HS impact messages simply being redundant with negative alliance quality). Specifically, we assessed the bivariate correlations between the early HS vector and all measures of alliance quality in this data set (i.e., early, middle, and late treatment). Results indicated no significant relations between the early HS vector and early alliance (r = –.023, p > .05), middle alliance (r = –.020, p > .05), or late alliance (r = .045, p > .05), thus suggesting the distinctness of these constructs.
Primary Analyses
To test our primary questions, we analyzed data using growth curve modeling in HLM 6. We fit a series of models to the HRSD data to determine the shape of patients' depression change trajectories over the treatment course. We compared a model including only linear change in depression to a quadratic model that accounted for the curvature in change, as well as linear change across the 12 treatment weeks. A chi-square comparison test between the deviance fit statistics for the two models indicated that the quadratic model was a significantly better fit to the data than the linear model, Δχ2(4, N = 256) = 238.045, p < .001.
Thus, the unconditional model selected for all subsequent analyses was the quadratic trajectory model, with variability to be predicted around the deviations from the average (i.e., all error terms were allowed to vary). The intercept, linear, and quadratic fixed effects were all significantly different from zero (see Table 1). On average, patients' depression rating at the midpoint (Week 6) of therapy was 18.27. The linear rate of change in depression was negative, with an average decrease of 1.36 in HRSD scores per week. The significant quadratic term was positive, indicating that, on average, the rate of depression deceleration started out more steeply and slowed toward the final therapy sessions. Also, as indicated in Table 1, tests of the variance components (random effects) confirmed that there was significant variability around the linear and curvilinear facets of depression change, suggesting that predictors could be added to the model to determine if patients varied systematically on relevant characteristics.
Baseline Quadratic Model of Depression Change Across 12 Treatment Weeks
Next, we added the predictors to the unconditional quadratic model. First, treatment condition (CBASP alone vs. combined CBASP plus medication) was added to the intercept, linear, and quadratic equations. Treatment condition was a significant predictor of the average depression level at treatment midpoint, the average rate of change at midpoint, and the average curvature of depression change throughout treatment (see the Treatment Model column in Table 2). Specifically, the combined group had a lower midpoint depression level than the CBASP alone group. Also, the shape of change for the CBASP alone group had less curvature in depression decline than the combined group. Finally, the combined group, relative to CBASP alone, had a more curved trajectory of change, with a steeper decline in depression early in therapy and a slower rate of change toward the later therapy sessions (see Figure 1). This treatment model was a significantly better fit to the data than the unconditional quadratic model, Δχ2(3, N = 256) = 13.396, p < .01. The effect size in growth curve models is represented by a pseudo change in R2 statistic, described in terms of how much of the variance in each aspect of patients' depression was explained by each predictor (i.e., depression at treatment midpoint, depression change at midpoint, and depression change curvature across treatment). The treatment condition predictor accounted for a 3.79% reduction of unexplained variance around the midpoint depression score (intercept), a 6.17% reduction of unexplained variance in the rate of depression change at midpoint (linear term), and a 3.01% reduction in unexplained variance around the curvature of depression change (quadratic term). These findings indicate that in addition to posttreatment depression outcome, the course of depression change is different depending on the type of treatment. Because the treatment effect was significant both at the level of fixed effects and in overall model fit, the treatment condition variable was retained for subsequent analyses.
Model Comparison and Parameters for Treatment Condition and Hostile–Submissive (HS) Change Predicting Depression Change Over 12 Weeks of Treatment
Figure 1. Depression change over 12 weeks for patients who received cognitive-behavioral analysis system of psychotherapy (CBASP) alone versus those who received the combination of CBASP plus nefazodone. HRS Depression = Hamilton Rating Scale for Depression.
Next, the HS change variable was added as a predictor to the model. The model estimated significant effects on the linear parameter, or rate of change in depression (see the Treatment and HS Change Model column in Table 2). Patients whose psychotherapists perceived less HS impact messages over the treatment course had significantly more reduction in depression. This effect accounted for an additional 6.23% of the variance in depression change above the variance explained by the model with only the treatment condition predictor. In addition, this model was a better fit to the data than the model with the treatment condition predictor alone, Δχ2(3, N = 26) = 12.53, p < .01. Finally, a model adding the interaction of HS change and treatment condition showed no significant interaction effect on any aspect of depression change over therapy. This suggests that the relation between change in HS impact messages and depression is the same for both treatment groups (see Figure 2). Given that combined treatment patients fared significantly better than CBASP only patients in terms of treatment depression reduction, the fact that the association between HS change and depression reduction is the same for both treatment groups suggests that HS change is not simply capturing symptom change.
Figure 2. Decreases in patients' hostile–submissive (HS) impacts predict greater depression reduction in both cognitive-behavioral analysis system of psychotherapy (CBASP) and combined CBASP plus nefazodone groups. Values for HS Decrease and HS Increase are the 10th and 90th percentiles, respectively. Med = medication; HRS Depression = Hamilton Rating Scale for Depression.
We also estimated a similar model testing whether change in FD over the treatment course predicted depression outcome over time. Results indicated no significant relation between FD change and either depression level at midpoint or change across treatment, and the model fit did not significantly improve from the model with only treatment condition as a predictor, Δχ2(3, N = 256) = 2.76, p > .50. A model testing whether there was an interaction between FD change and treatment condition similarly showed no significant interaction effect on any of the depression level or change parameters.
Finally, we conducted logistic regression analyses to predict the probability that a patient would respond to treatment. We compared a model with treatment condition and HS change to a model with the treatment predictor alone. The model with both the HS change and treatment condition predictors was a statistically significant improvement over the model with only the treatment condition predictor, χ2(1, N = 259) = 5.254, p = .02. The model correctly classified 69% of responders versus nonresponders. Table 3 shows the logistic regression coefficient, Wald test, and odds ratio for each of the predictors. Both the treatment condition and HS change predictors had significant partial effects. As in the growth curve model, being in the combined CBASP and medication condition versus CBASP alone was associated with a greater probability of responding to treatment. Decreases in HS impact messages over therapy were also associated with a greater probability of responding to treatment (see Figure 3). A logistic regression testing whether FD change predicted response did not significantly improve the model fit compared to the model with only the treatment condition predictor, χ2(1, N = 259) = 0.880, p = .35.
Logistic Regression Predicting Treatment Response From Treatment Condition and Hostile–Submissive (HS) Change Over Treatment
Figure 3. Probability of responding to treatment based on change in hostile–submissive impacts for cognitive-behavioral analysis system of psychotherapy (CBASP) alone versus CBASP plus nefazodone groups.
DiscussionThe goal of this study was to assess whether changes in chronically depressed patients' interpersonal impact messages (namely hostile–submissive [HS] and friendly–dominant [FD]), as perceived by their psychotherapists, were associated with depression change and treatment response status in theoretically consistent ways following 12 weeks of CBASP or CBASP plus nefazodone. As predicted, a decrease in HS impact messages was associated with greater depression reduction over time and with positive posttreatment response, irrespective of treatment condition. Counter to our prediction, an increase in FD impact messages was unrelated to depression reduction and posttreatment response. Our findings were also consistent with previous analyses conducted on the full modified ITT sample from which the current subsample derives (see Keller et al., 2000); our subsample patients in the combined treatment group evidenced better depression outcome at midpoint and across time than patients in the CBASP only group.
Consistent with our previous findings (Constantino et al., 2008), but with a different methodology (HLM-derived change scores vs. mixed analyses of variance), we found that patients' HS impact messages as perceived by their psychotherapists (across both treatment conditions) decreased significantly from early to late treatment, while their FD impact messages increased significantly (across both treatment conditions). Such changes suggest that these chronically depressed patients became, at least as perceived by their psychotherapists, more affiliative and balanced in their self–other reliance; a theoretically adaptive blend of interpersonal functioning that should theoretically promote greater interpersonal effectiveness and a corresponding decrease in depressive symptomatology (McCullough, 2000). And, as predicted, the decrease in HS impact messages perceived by their psychotherapists was associated with a faster reduction in depression over treatment and with positive therapeutic response.
It is possible that the specific interpersonal foci of CBASP (including SA, the IDE, and BST/R), as intended, promote greater affiliation and reduced hostility on the part of chronically depressed patients. Of course, it is important to stress that this study did not isolate the specific therapist, patient, dyadic, and/or treatment processes that led to the interpersonal changes; thus, future research will need to focus on the specific processes that causally foster the intended interpersonal effect of CBASP. One line of such mechanism work could focus on the quality of the therapeutic alliance. It is possible that a quality alliance provides an interpersonal vehicle through which an individual can change their HS ways of relating to others. In this sense, decreased HS might mediate the known association between alliance quality and outcome in Keller et al.'s (2000) chronic depression sample (see Klein et al., 2003). For now, the present findings suggest that an effect on interpersonal impacts is present, and that the effect (at least with regard to decreased HS impact messages) is associated with reduced depression. Of course, it is also important to emphasize that the correlational nature of the study cannot rule out the possibility of reverse causation (i.e., that reduced depression promotes changes in therapists' ratings of their patients' interpersonal impact messages). The present study could also not fully tease apart the respective contributory roles of HS impact messages and alliance on treatment outcome.
Unexpectedly, increased FD impact messages did not predict depression reduction or treatment response. This lack of association, coupled with the HS findings, could mean that change on the affiliation dimension (i.e., becoming less hostile and more affiliative) has a more profound interpersonal impact than increasing dominance or assertiveness. It might be that hostility is more readily detected and thus more aversive to deal with in interpersonal exchanges than problems in assertiveness. Thus, reductions in hostility might bring about more improvements in interpersonal functioning than increases in assertiveness and, consequently, relate to greater depression reduction. The present findings are consistent with those reported by Vittengl, Clark, and Jarrett (2004) in a study of cognitive therapy for recurrent depression. In their study, the researchers found that both self-directed affiliation and autonomy (a construct roughly comparable to friendly–dominant) increased significantly over 12 weeks of acute phase treatment. However, in examining the association of these variables with depression, only affiliation level at treatment's end predicted positive response status. Again, it seems possible that change in affiliation (both toward self and others) is more important for depression change than an increase in dominance/autonomy-taking. To the extent that this finding is accurate, and can be replicated, it is possible that the efficacy of CBASP could be improved by focusing its interpersonal strategies more centrally on the affiliation/hostility dimension. It is important to consider refinements for CBASP given its currently mixed efficacy findings (cf. Keller et al., 2000; Kocsis et al., 2009).
This perspective on affiliation is also consistent with our previous finding that the affiliation dimension may be the most central factor differentiating chronic depressives' pathology from normal functioning (Constantino et al., 2008). Thus, it would follow that change on this dimension might have the most significant influence on depression reduction. Of course, as vectors on a circumplex, the notion of friendly–dominance also includes increased affiliation coupled with autonomous acting on others. Thus, it could still be the case that greater friendly–dominance has an adaptive influence on chronically depressed patients. However, it may be that greater change is required before affecting depression. This is perhaps not surprising given Constantino et al.'s (2008) finding that FD impacts remained, in the same chronic depression sample as the current study, distinctly lower at treatment's end than a normative comparison group. It might also be that such changes in assertiveness simply take longer than the 12 weeks in the present treatment. Although patients might be on their way to greater assertiveness following 12 weeks, they might need more time and opportunities to practice these skills in a manner that will relate to significant changes in interpersonal relating. Such time and opportunity might require a longer course of CBASP. Alternatively, the current treatment length might be sufficient to learn these skills, but more time is needed to reap their benefits in naturalistically occurring relationships. This is a question for future study.
The difference in findings for HS and FD impact messages might also be a function of the special nature of the therapeutic relationship. The patient–psychotherapist relationship is inherently asymmetrical, and detecting increased assertiveness in this specific context might be more difficult than detecting reductions in hostility. Thus, it is possible that even more changes in assertiveness were present than detected by the clinicians. With more sensitive detection, such changes might significantly predict depression outcomes. Clinically then, it might be important for CBASP clinicians to pay close attention to subtle themes of patient assertiveness, especially as they relate to the work being done in the context of the psychotherapeutic relationship (e.g., the IDE). Again, it is important to consider refinements for CBASP, and to conduct additional research on its process of change, given its currently mixed efficacy findings. Finally, another possible explanation of the non-finding for FD impact messages is that increased friendly–dominance has adaptive clinical consequences not captured by a pure depression measure. For example, it is plausible that increased FD impact messages could affect other constructs like increased quality of life or increased relational satisfaction, which could be indirectly associated with depression reduction or even relapse prevention. It will be important for future research to examine interpersonal styles and impacts in relation to clinical constructs other than just depression.
Secondarily, our findings also provided further information on the nature of depression change as a complement to Keller et al.'s (2000) results. For the full modified ITT sample, Keller et al. used a piecewise mixed effects linear model to examine differences between their three treatment conditions on linear rate of depression change from baseline to Week 4 and then from Week 4 to Week 12 (or the final visit). Week 4 was selected because it was the earliest time that the nefazodone was predicted to have a therapeutic effect. Relevant to the current study, these authors found that patients in combined CBASP plus nefazodone evidenced a greater rate of depression reduction from baseline to Week 4 than patients in CBASP alone. This rate difference, however, was not statistically significant from Week 4 to Week 12. In the current study, the findings based on our subsample essentially paralleled those of Keller et al. In particular, patients receiving combined treatment had significantly lower depression levels and rate of depression change at the midpoint of therapy compared to patients in CBASP alone. Note that we examined the actual midpoint of Week 6 compared to Week 4 in Keller et al.'s analysis. Thus, the during-treatment effect of treatment condition on depression change was still evident 2 weeks further into the treatment. We also examined the quadratic term, which suggested that the rate of depression change was different for the two groups. Change in CBASP alone involved a fairly uniform deceleration, while the combined group had a more curved trajectory—that is, a steeper deceleration in depression earlier in treatment, which slowed toward the end of treatment. Clinically, the findings point to a more efficient response to combined CBASP and nefazodone for chronically depressed patients, which can be useful both in treatment selection and planning as well as in response/non-response monitoring.
The current study has several limitations. First, the IMI assesses interpersonal functioning from just one perspective and within one relationship (in this case, the clinician's perspective in the context of a therapy relationship), thus restricting the generalizability of such assessment to the patient's own experience and to other important relationships. Furthermore, the psychotherapists' IMI ratings could be biased both early in treatment (when knowledge about the theory of chronic depression could influence ratings) and late in treatment (when expectations that the patient has improved in theory-consistent ways could influence ratings). Thus, the present findings should be interpreted cautiously, and multi-method/multi-perspective replication is required for greater confidence. It should be noted, though, that the present findings were quite similar to those in a study of depressed outpatients whose significant others rated their interpersonal impact messages at both pre- and posttreatment (Grosse Holtforth, Altenstein, Ansell, Schneider, & Caspar, 2011). In this study, patients were perceived by their significant others as less friendly–submissive, submissive, and hostile–submissive, and more dominant and friendly–dominant after treatment. Moreover, decreases in submissiveness and hostile–submissiveness were associated with greater depression reduction. Thus, some converging findings across rating perspectives already exist.
Second, the IMI was measured on just two occasions, thus limiting our ability to measure more complex change patterns. Although using HLM to create change scores helped us to remove measurement error, we were restricted to just one early and one late treatment assessment. Interpersonal change might unfold more complexly in the context of the patient–psychotherapist relationship, including likely ebbs and flows as the dyad engages in novel exchanges that disrupt the patient's maladaptive transaction cycles. These complexities might play a role in how depression change occurs, and they might also interact with other relational processes such as alliance ruptures and potential subsequent repairs. Having just the two IMI occasions also allowed us to examine only the concurrent relation of interpersonal change and depression change, thus limiting our ability to assess fully the temporal direction of the change.
Third, a large portion of cases in our effective sample had only one IMI rating (with change scores being estimated using characteristics of the entire sample). This scenario was predominantly a function of patients dropping out prior to the late IMI assessment. Thus, it is unclear how the IMI change trajectories might have continued for these dropout cases, and how these specific trajectories might have differentially related to depression. In future studies, it will be important to have more frequent and reliable IMI assessment to understand more fully (with less missing data and more data points when imputation is required) how IMI change relates to therapeutic change.
Fourth, given that patients included in our subsample were more symptomatic than patients excluded because of no IMI ratings, it is unclear if our findings would generalize to patients with less severe chronic depression.
Fifth, it is possible that the IMI items, perhaps especially for the FD scale, were difficult for the rating therapists to apply to the therapy setting. It is plausible that such difficulty contributed to the lack of association between FD change and outcome.
Sixth, interrater reliability on the HRSD was not assessed in the parent trial from which the current data derive.
Seventh, given the absence of a control condition, it is difficult to know if the changes that we did capture in interpersonal impacts were specific to CBASP versus more generally related to having 12 weeks of contact with a psychosocial or pharmacological treatment provider.
Finally, it is possible that IMI changes were at least partly attributable to statistical regression to the mean or to repeated administration.
With these limitations in mind, the present study provides further support for McCullough's (2000) theory of chronic depression and therapeutic change; it also provides preliminary, though non-causal, evidence for the promise of CBASP influencing processes and outcomes in its intended manner. With continued focus on the nature of chronic depression and its related treatment processes and outcomes, this debilitating condition will no longer require the “understudied” designation.
Footnotes 1 The large amount of missing IMI data in Keller et al.'s (2000) trial can likely be attributed to the fact that IMI ratings were not part of the official research protocol, and, thus, their collection was not closely monitored by the research team. Rather, IMI ratings were used to facilitate formulation of the transference hypothesis as per the CBASP manual. Because 59% of the therapists did complete at least one IMI, we were able to use this clinically rich measure in the present secondary process analyses.
2 However, to ensure that our handling of missing data did not radically alter the results, we re-ran our primary analyses (discussed below) separately on two subgroups from our overall sample of 259. The first subgroup included only patients (N = 141) whose IMI change scores were derived from complete IMI data. The second subgroup included only patients (N = 118) whose IMI change scores were estimated because IMI data existed at only one of the two time points. Results, across all primary analyses, showed similar patterns in both subgroups to the full sample findings that are reported below.
3 Preliminary analyses also indicated a significant improvement in model fit with the addition of a cubic term to the growth model. However, there was limited variability around the cubic term, and we failed to find any significant relationships between our predictors and cubic change. Thus, we elected to present findings on a quadratic model for the sake of clarity and parsimony.
4 We use the term unconditional to describe the selected Level 1 model with no Level 2 predictors. Technically, this is a baseline, rather than unconditional, model because there are Level 1 predictors of time included. However, we elected to use unconditional to avoid any confusion in meaning with the term baseline within the context of a treatment study.
5 Note that the unstandardized coefficient for linear change in depression is appropriately documented in Table 1 as −13.581. The discrepancy is because we divided the variable used to model weeks in psychotherapy by 10 to avoid estimation problems that can arise from an ill-scaled matrix.
6 We also examined the degree to which early (Week 1) HS scores correlated with early (Week 1) HRSD scores, as it would be concerning if the most depressed patients at baseline were also viewed as the most HS, and that regression to the mean on both variables might account for higher levels of change on both. A bivariate Pearson correlation indicated only a slight association between these variables, which was not statistically significant (r = .119, p = .07). This suggests that the HS and depression constructs are distinct at the beginning of treatment, which mitigates the likelihood that regression to the mean on both variables is responsible for the findings presented.
7 Given the aforementioned significant association between early alliance quality and posttreatment outcome in this data set, we also conducted all of our primary analyses controlling for early alliance. The inclusion of early alliance did not change the findings, thus providing further evidence that the IMI vectors (especially HS, which has a conceptually face valid connection to alliance components) are not redundant.
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Submitted: September 27, 2010 Revised: January 11, 2012 Accepted: March 13, 2012
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Source: Journal of Consulting and Clinical Psychology. Vol. 80. (3), Jun, 2012 pp. 354-364)
Accession Number: 2012-10796-001
Digital Object Identifier: 10.1037/a0028351
Record: 53- Title:
- The relationship between nonsuicidal self-injury and attempted suicide: Converging evidence from four samples.
- Authors:
- Klonsky, E. David. Department of Psychology, University of British Columbia, Vancouver, BC, Canada, edklonsky@psych.ubc.ca
May, Alexis M.. Department of Psychology, University of British Columbia, Vancouver, BC, Canada
Glenn, Catherine R.. Department of Psychology, Harvard University, Cambridge, MA, US - Address:
- Klonsky, E. David, Department of Psychology, University of British Columbia, 2136 West Mall, Vancouver, BC, Canada, V6T 1Z4, edklonsky@psych.ubc.ca
- Source:
- Journal of Abnormal Psychology, Vol 122(1), Feb, 2013. pp. 231-237.
- NLM Title Abbreviation:
- J Abnorm Psychol
- Page Count:
- 7
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- The Journal of Abnormal and Social Psychology; The Journal of Abnormal Psychology; The Journal of Abnormal Psychology and Social Psychology
- ISSN:
- 0021-843X (Print)
1939-1846 (Electronic) - Language:
- English
- Keywords:
- nonsuicidal self-injury, risk assessment, suicide, risk factors, attempted suicide, adolescent psychiatric patients
- Abstract:
- Theoretical and empirical literature suggests that nonsuicidal self-injury (NSSI) may represent a particularly important risk factor for suicide. The present study examined the associations of NSSI and established suicide risk factors to attempted suicide in four samples: adolescent psychiatric patients (n = 139), adolescent high school students (n = 426), university undergraduates (n = 1,364), and a random-digit dialing sample of United States adults (n = 438). All samples were administered measures of NSSI, suicide ideation, and suicide attempts; the first three samples were also administered measures of depression, anxiety, impulsivity, and borderline personality disorder (BPD). In all four samples, NSSI exhibited a robust relationship to attempted suicide (median Phi = .36). Only suicide ideation exhibited a stronger relationship to attempted suicide (median Phi = .47), whereas associations were smaller for BPD (median rpb = .29), depression (median rpb = .24), anxiety (median rpb = .16), and impulsivity (median rpb = .11). When these known suicide risk factors and NSSI were simultaneously entered into logistic regression analyses, only NSSI and suicide ideation maintained significant associations with attempted suicide. Results suggest that NSSI is an especially important risk factor for suicide. Findings are interpreted in the context of Joiner's interpersonal-psychological theory of suicide; specifically, NSSI may be a uniquely important risk factor for suicide because its presence is associated with both increased desire and capability for suicide. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Attempted Suicide; *Risk Factors; *Self-Injurious Behavior; *Suicide; *Risk Assessment; Psychiatric Patients
- Medical Subject Headings (MeSH):
- Adolescent; Adult; Aged; Anxiety; Borderline Personality Disorder; Depression; Female; Humans; Impulsive Behavior; Male; Middle Aged; New York; Risk Factors; Self-Injurious Behavior; Students; Suicidal Ideation; Suicide, Attempted; Young Adult
- PsycINFO Classification:
- Behavior Disorders & Antisocial Behavior (3230)
- Population:
- Human
Male
Female
Inpatient - Location:
- US
- Age Group:
- Adolescence (13-17 yrs)
- Tests & Measures:
- Youth Risk Behavior Survey
Structured Interview for DSM–IV Personality
Patient Health Questionnaire–Adolescent Version
McLean Screening Instrument for Borderline Personality Disorder
Inventory of Statements About Self-Injury DOI: 10.1037/t32941-000
Trauma Symptom Inventory
Structured Clinical Interview for DSM-IV Axis I Disorders
Mini International Neuropsychiatric Interview DOI: 10.1037/t18597-000 - Grant Sponsorship:
- Sponsor: National Institute of Mental Health
Grant Number: MH0800960
Recipients: Klonsky, E. David - Methodology:
- Empirical Study; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Oct 15, 2012; Accepted: Aug 21, 2012; Revised: Aug 16, 2012; First Submitted: Apr 17, 2012
- Release Date:
- 20121015
- Correction Date:
- 20140915
- Copyright:
- American Psychological Association. 2012
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0030278
- PMID:
- 23067259
- Accession Number:
- 2012-27535-001
- Number of Citations in Source:
- 39
- Persistent link to this record (Permalink):
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- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2012-27535-001&site=ehost-live">The relationship between nonsuicidal self-injury and attempted suicide: Converging evidence from four samples.</A>
- Database:
- PsycINFO
The Relationship Between Nonsuicidal Self-Injury and Attempted Suicide: Converging Evidence From Four Samples
By: E. David Klonsky
Department of Psychology, University of British Columbia;
Alexis M. May
Department of Psychology, University of British Columbia
Catherine R. Glenn
Department of Psychology, Harvard University
Acknowledgement: There are no conflicts of interest to report. The research was supported in part by grant MH0800960 awarded to E. David Klonsky from the National Institute of Mental Health.
Nonsuicidal self-injury (NSSI; e.g., cutting, burning) refers to the intentional destruction of one's own body tissue without suicidal intent and for purposes not socially sanctioned (Klonsky & Olino, 2008; Klonsky, Oltmanns, & Turhkeimer, 2003; Nock & Favazza, 2009). Rates of NSSI are estimated at 4–6% in the adult general population and 20% in adult patient populations (Briere & Gil, 1998; Klonsky, 2011; Klonsky et al., 2003). However, NSSI appears to be disproportionately prevalent in adolescents and young adults: approximately 14–17% of adolescents and young adults report having self-injured (Whitlock, Eckenrode, & Silverman, 2006), and rates approach 40% or higher in adolescent inpatient samples (DiClemente, Ponton, & Hartley, 1991; Klonsky & Muehlenkamp, 2007). Because NSSI has been observed to occur in a variety of diagnostic contexts, and because NSSI itself is associated with distress and impairment irrespective of co-occurring diagnosis (Klonsky & Olino, 2008; Nock, Joiner, Gordon, Lloyd-Richardson, & Prinstein. 2006), NSSI has been proposed as its own behavioral syndrome for the next edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) (Shaffer & Jacobson, 2009).
NSSI and SuicideThe relationship between NSSI and attempted suicide is complex. On the one hand, the two behaviors often co-occur (Klonsky & Muehlenkamp, 2007; Nock et al., 2006; Whitlock et al., 2006) and share a salient surface-level similarity in that they are both forms of self-inflicted physical violence. For this reason, some researchers have regarded all forms of self-injurious behavior as falling along a suicidal spectrum regardless of intent (Hawton, Rodham, Evans, & Weatherall, 2002). On the other hand, NSSI and attempted suicide have important differences. For example, the behaviors differ in terms prevalence (NSSI is more prevalent), frequency (NSSI is often performed dozens or hundreds of times whereas suicide attempts are typically performed once or a few times), methods (cutting and burning are more characteristic of NSSI whereas self-poisoning is more characteristic of attempted suicide), severity (NSSI rarely causes medically severe or lethal injuries), and functions (NSSI is performed without intent to die, and sometimes as an attempt to avoid suicidal urges) (CDC, 2010; Favazza, 1998; Klonsky, 2007; Klonsky & Muehlenkamp, 2007; Muehlenkamp, 2005). A primary aim of the DSM-5 proposal is to highlight these distinctions between NSSI and attempted suicide (Shaffer & Jacobson, 2009).
Accurately characterizing the relationship between NSSI and attempted suicide—both their distinctiveness and overlap—is essential for research and intervention. There is concern that the historical tendency to classify or misidentify NSSI as attempted suicide has led to inaccurate epidemiological estimates of suicidal behaviors (Shaffer & Jacobson, 2009). In clinical settings, mistaking NSSI for attempted suicide can lead to unnecessary and potentially iatrogenic hospitalizations, inaccurate case conceptualization and treatment planning, and misallocation of valuable emergency resources. At the same time, a perspective that overemphasizes the behaviors' independence and ignores potential comorbidity between NSSI and attempted suicide could mean ignoring a valuable indicator of suicide risk. The proposed research was designed to address this need.
There are both theoretical and empirical reasons why NSSI may represent a particularly robust risk factor for attempted suicide. Joiner's interpersonal theory of suicide (2005; Van Orden et al., 2010) states that a suicide attempt requires both the desire and capability for suicide. Unlike risk factors such as depression, which confers increased desire but not capability for a suicide attempt, or risk factors such as access to firearms, which confers increased capability but not desire, NSSI may be relatively unique among suicide risk factors in that it serves as a marker for both increased desire and capability. Specifically, NSSI is associated with elevated emotional and interpersonal distress (Klonsky & Olino, 2008; Klonsky et al., 2003; Klonsky & Muehlenkamp, 2007), which increases the likelihood of suicide ideation (i.e., desire), and NSSI facilitates habituation to self-inflicted violence and pain, which increases the ability to attempt suicide (i.e., capability) (Nock et al., 2006). Indeed, two recent studies found that NSSI prospectively predicted attempted suicide more strongly than other suicide risk factors (Asarnow et al., 2011; Wilkinson, Kelvin, Roberts, Dubicka, & Goodyer., 2011). Elucidating the relation of NSSI to attempted suicide is essential for both research and treatment.
Study AimsOur primary aim was to determine the strength of the association between NSSI and attempted suicide. The use of four diverse samples—adolescent psychiatric patients, adolescent high school students, university undergraduates, and a random-digit dialing sample of U.S. adults—enhances the generalizability of results and helps ensure that findings are broadly relevant for theoretical and clinical models of suicide risk.
In addition, we compared the association between attempted suicide and NSSI to the associations between attempted suicide and established suicide risk factors, specifically suicide ideation, depression, anxiety, impulsivity, and borderline personality disorder (BPD) symptoms. These analyses help clarify the importance of NSSI for conferring suicide risk relative to known suicide risk factors. We chose suicide ideation, depression, anxiety, impulsivity, and BPD as comparison variables for two reasons. First, they are highlighted in published guidelines for conducting suicide risk assessments (American Psychiatric Association, 2006; Rudd et al., 2006). Second, they are indicators of emotional distress and personality pathology, which are correlates of both NSSI and attempted suicide, and could potentially account for the relationship between the two behaviors.
MethodThe present study utilized data from four separate samples. IRB approval was received from all relevant institutions before data collection commenced. In all four samples attempted suicide, suicide ideation, and NSSI were assessed; in addition, depression, anxiety, impulsivity, and BPD were assessed in samples 1–3. The sample sizes and specific measures for each sample are described below.
Sample 1. Adolescent Psychiatric Inpatients Participants
Participants were 171 adolescent psychiatric patients (consecutive admissions to adolescent inpatient and partial hospitalization units at South Oaks Hospital in Amityville, NY). Adolescents were only excluded if they were unable to complete the protocol due to severe psychosis, aggressive behavior, cognitive deficits, or suicide-related behavior that the staff deemed too extreme to participate in the study. Permission from parents/guardians of participants was obtained at the time of admission to the facility, and assent was obtained before measures were administered. Participants were 70% female, 61% Caucasian, 21% Hispanic, 12% African American, with a mean age of 15.1 (SD = 1.4). Fifty-nine percent reported NSSI; the most common forms were cutting and banging/hitting, endorsed by 86% and 53%, respectively, of those reporting NSSI. Sixty percent of participants reported a history of suicide ideation, and 40% reported a suicide attempt.
Measures
Suicide ideation and attempts
The Youth Risk Behavior Survey (YRBS; Brener et al., 2002) was developed by the U.S. Centers for Disease Control to assess health-risk behaviors, including suicidality. A history of suicide ideation is measured by the item: “Have you ever seriously thought about killing yourself?” A history of attempted suicide is measured by the item: “Have you ever tried to kill yourself?” The YRBS also includes items assessing 12-month ideation and attempts, as well as medical severity of attempts. YRBS suicide questions have good reliability and validity (Brener et al., 2002; May & Klonsky, 2011).
Nonsuicidal self-injury
The Inventory of Statements About Self-injury (ISAS; Klonsky & Glenn, 2009; Klonsky & Olino, 2008) assesses the lifetime frequency of 12 different NSSI behaviors performed “intentionally (i.e., on purpose) and without suicidal intent (i.e., not for suicidal reasons).” These behaviors include banging/hitting self, biting, burning, carving, cutting, wound picking, needle-sticking, pinching, hair pulling, rubbing skin against rough surfaces, severe scratching, and swallowing chemicals. The ISAS behavioral scales have demonstrated good reliability and validity (Klonsky & Olino, 2008).
Depression and anxiety
The MINI International Neuropsychiatric Interview is a reliable and valid structured interview (Sheehan et al., 1998) of Axis I psychopathology. Interviews were conducted by a clinical psychology doctoral student trained to reliability (i.e., rs > .90 with other trained interviewers). The MINI major depression diagnosis was utilized to index depression, and the MINI generalized anxiety disorder diagnosis was utilized to index anxiety. We chose GAD as opposed to other anxiety disorder diagnoses because we felt it was the best indicator of the general construct of anxiety.
Impulsivity
The UPPS impulsive behavior scale (Whiteside & Lynam, 2001) is a 45-item self-report measure of four distinct personality pathways to impulsive behavior: Urgency (tendency to give in to strong impulses when experiencing intense negative emotions), Perseverance (ability to persist in completing jobs or obligations despite boredom or fatigue), Premeditation (ability to think through potential consequences of behavior before acting), and Sensation Seeking (preference for excitement and stimulation). The UPPS scale has strong psychometric properties (Whiteside & Lynam, 2001). The total UPPS score was utilized to index an overall disposition for impulsive behaviors.
Borderline personality disorder
The Structured Interview for DSM–IV Personality (SIDP-IV) is a validated structured interview assessing personality disorders (Pfohl, Blum, & Zimmerman, 1997). Interviews were conducted by a clinical psychology doctoral student trained to reliability (i.e., rs > .90 with other trained interviewers). Scores for the BPD items were summed to provide a dimensional measure of BPD; the suicide/self-injury criterion was omitted to avoid confounding results.
Sample 2. Community Sample of Adolescents Participants
Participants were 428 students from a large high school east of New York City. Parental/guardian consent and adolescent assent were obtained for all participants. Participants were 61% female, 53% Caucasian, 19% Hispanic, 15% Asian, 11% African American, and 3% mixed racial heritage, and participants' age ranged from 13–17 (reflects age range of target population; age data were not obtained from participants). Twenty-one percent reported NSSI; the most common forms of NSSI were cutting and banging/hitting, endorsed by 52% and 56%, respectively, of those reporting NSSI. Sixteen percent of participants reported a history of suicide ideation, and 5% reported a suicide attempt.
Measures
Suicide ideation and attempts
Same measure as for Sample 1.
Nonsuicidal self-injury
Same measure as for Sample 1, except that only seven rather than 12 NSSI behaviors were assessed: banging/hitting self, biting, burning, carving, cutting, rubbing skin against rough surfaces, and severe scratching (the following were not assessed: hair pulling, needle-sticking, pinching, swallowing chemicals, wound picking).
Depression and anxiety
The Patient Health Questionnaire–Adolescent Version (PHQ-A; Johnson, Harris, Spitzer, & Williams, 2002) is a self-report questionnaire developed by the authors of the Structured Clinical Interview for DSM–IV (SCID-I) to assess four classes of Axis I disorders: mood, anxiety, eating, and substance/alcohol. The PHQ-A major depressive disorder items were summed to form a dimensional index of depression symptoms, and the PHQ-A generalized anxiety disorder items were summed to form a dimensional index of anxiety symptoms. As for Sample 1, we chose GAD as opposed to other anxiety disorder diagnoses because we felt it was the best indicator of the general construct of anxiety. The PHQ-A has demonstrated excellent correspondence with structured interview measures of Axis I disorders (Johnson et al., 2002).
Impulsivity
Same measure as for Sample 1 (UPPS), but a 16-item short-version developed by using the four items from each scale with the highest loadings in Whiteside and Lynam (2001). This short version has demonstrated excellent psychometric properties in two previous studies of NSSI and suicide (Glenn & Klonsky, 2010; Klonsky & May, 2010).
Borderline personality
The McLean Screening Instrument for Borderline Personality Disorder (MSI-BPD) is a 10-item self-report measure of BPD features that has shown excellent correspondence with diagnoses made by validated structured interview (Zanarini et al., 2003). For the present study, the suicide/self-injury criterion was omitted to avoid confounding results.
Sample 3. University Undergraduates Participants
Participants were 1,656 university undergraduates participating in an Internet-based survey on substance use via a secure website. Upon accessing the survey, students provided informed consent. Participants were 56% female, 43% Caucasian, 35% Asian, 7% African American, 9% Hispanic, and 7% from other ethnic categories, with a mean age of 20.7 (SD = 2.0). Twenty percent of participants reported NSSI, 17% a history of suicide ideation, and 7% a suicide attempt.
Measures
Suicide ideation and attempts
Same measure as for Samples 1 and 2.
Nonsuicidal self-injury
An item from the Trauma Symptom Inventory that was utilized in two previous epidemiologic studies of NSSI (Briere & Gil, 1998; Klonsky, 2011) was used in the present sample: “In your lifetime, how often have you intentionally hurt yourself—for example, by scratching, cutting, or burning—even though you were not trying to commit suicide?” This question is similar to the item used in a recent epidemiologic study of NSSI in United States adults (Klonsky, 2011), except that the item in the present study used the following slightly modified response options: [a] never, [b] once, [c] twice, [d] 3–5 times, [e] 6–9 times, [f] 10 or more times. Data on specific NSSI methods were not obtained.
Depression and anxiety
Same measures as for Sample 2.
Impulsivity
Same measure as for Sample 2.
Borderline personality
Same measure as for Sample 2.
Sample 4. Random-Digit Dialing Sample of United States Adults Participants
Participants were 439 U.S. adults recruited via an equal-probability random-digit dialing procedure as part of an epidemiologic study of NSSI (Klonsky, 2011). Participants were 61% female, 86% Caucasian, 6% African American, 3% Hispanic/Latino, 1% Asian American, and 1% Native American, and mean age was 55.5 (SD = 16.6). Six percent reported NSSI; the most common forms were cutting and scratching, each endorsed by 35% of those who reported NSSI. Seventeen percent of participants reported a history of suicide ideation, and 3% reported a suicide attempt.
Measures
Suicide ideation and attempts
Suicide ideation and attempts were assessed with the following items utilized in the National Comorbidity Survey (Kessler, Borges, & Walters, 1999): “Have you ever seriously thought about committing suicide?” and “Have you ever attempted suicide?”
Nonsuicidal self-injury
Same measure as in Sample 3, except with the following slightly modified response options: [a] 0 times, [b] between 1 and 4 times, [c] between 5 and 9 times, [d] between 10 and 50 times, [e] more than 50 times.
The additional clinical variables assessed in Samples 1, 2, and 3—depression, anxiety, impulsivity, and BPD—were not assessed in Sample 4.
Data AnalysisThe same analytic procedures were utilized for all samples so that results are comparable across samples. NSSI, attempted suicide, and suicide ideation were each treated as dichotomous variables (present if any lifetime instance was reported). Phi coefficients were utilized to examine associations between dichotomous variables, and point-biserial correlations were used to examine associations between dimensional and dichotomous variables. Coefficient alpha for all dimensional measures exceeded .74 (details on internal consistencies, descriptive statistics, and intercorrelations for all study variables are available from the corresponding author). Only participants with complete suicide data were included in analyses; thus, inclusion rates were 81.3% for Sample 1 (n = 139), 98.4% for Sample 2 (n = 426), 81.6% for Sample 3 (n = 1,351), and 99.8% for Sample 4 (n = 438).
ResultsWe first examined the association of attempted suicide to NSSI, suicide ideation, depression, anxiety, impulsivity, and BPD. Complete results are presented in Table 1. For the relation of NSSI to attempted suicide, Phi ranged from .28 (undergraduates) to .50 (adolescent psychiatric patients), with a median of .36. This was slightly smaller in magnitude than the effect size for suicide ideation (median Phi = .47), but larger than the effect sizes for BPD (median rpb = .29), depression (median rpb = .24), anxiety (median rpb = .16), and impulsivity (median rpb = .11).
Relation of Nonsuicidal Self-Injury (NSSI) and Other Suicide Risk Factors to Lifetime Attempted Suicide in Four Samples
Next, following the procedures of Steiger (1980), we examined whether the association between NSSI and attempted suicide varied by gender (see Table 2). For the high school sample, the association between NSSI and attempted suicide was higher for girls (.46) than for boys (.22), p = .007. The association did not vary significantly by gender in each of the other three samples.
Relation of Nonsuicidal Self-Injury (NSSI) to Attempted Suicide for Females vs. Males
Finally, we utilized simultaneous logistic regressions to examine the unique contributions of NSSI, suicide ideation, BPD, depression, anxiety, and impulsivity in the prediction of attempted suicide. (Sample 4 was excluded because it lacked measures of BPD, depression, anxiety, and impulsivity.) Complete results are presented in Table 3. Notably, in all three samples, only NSSI and suicide ideation retained statistically significant unique associations with attempted suicide (all ps < .05).
Logistic Regression Analyses Examining Unique Contributions of Nonsuicidal Self-Injury (NSSI) and Known Suicide Risk Factors to the Prediction of Attempted Suicide
DiscussionThis study examined the relationship between NSSI and attempted suicide in four samples: adolescent psychiatric patients, adolescent high school students, university undergraduates, and U.S. adults. In all four samples, NSSI exhibited a reliable and moderate relationship with attempted suicide. This relationship was slightly smaller than that of suicide ideation to attempted suicide, and larger than the relationships of depression, anxiety, impulsivity, and BPD to attempted suicide. When all risk factors were simultaneously entered into a logistic regression, only NSSI and suicide ideation maintained a unique relationship with attempted suicide. Taken together, findings suggest that the relationship of NSSI to attempted suicide is particularly strong, second in magnitude only to suicide ideation.
Joiner's interpersonal theory of suicide (Joiner, 2005; Van Orden et al., 2010) provides one useful context for interpreting these results. According to this theory, attempting suicide requires both the desire and capability to attempt suicide. NSSI may stand out among risk factors for suicide because it correlates with both suicidal desire and capability: NSSI indicates heightened risk for suicidal desire through its association with emotional and interpersonal distress (Klonsky et al., 2003; Klonsky & Muehlenkamp, 2007; Klonsky & Olino, 2008), and NSSI raises capability by allowing individuals to habituate to self-inflicted pain and violence (Nock et al., 2006). In short, when it comes to suicide risk, NSSI may represent “double trouble” (term suggested by B. Walsh, personal communication, April 22, 2010) in that it confers risk for both suicidal desire and capability.
Other interpretations also warrant consideration. For example, general tendencies toward harmful behaviors and emotion dysregulation may represent third variables contributing to the NSSI–suicide relationship. However, it is notable that NSSI maintained a relationship to attempted suicide above and beyond the other constructs examined, given that the measures of these constructs include items related to harmful behaviors and emotion dysregulation. Another potential third variable is shame. Shame is often present in both NSSI and attempted suicide (Brown, Linehan, Comtois, Murray, & Chapman, 2009), and is reflected in the self-punishment motivations commonly endorsed for both behaviors (Brown, Comtois, & Linehan, 2002). It will be important for future research to address these and other potential third variables, especially those associated with both emotion dysregulation and bodily harm, such as eating and substance disorders.
Findings also suggest that NSSI confers risk for attempted suicide across different sociodemographic and clinical groups. The present study found strong relationships between NSSI and attempted suicide in both adolescents and adults, men and women, and treatment and community populations, suggesting results are likely to be generalizable across diverse populations. Interestingly, in the adolescent community sample, the association was stronger for girls than boys. We speculate that NSSI more strongly increases capability to attempt suicide for adolescent girls than boys. Adolescent boys engage in a larger quantity and variety of risky and harmful behaviors as compared to girls (e.g., fighting, substance use; Brener & Collins, 1998; Wu, Rose, & Bancroft, 2006). Thus, for boys, NSSI is just one of many ways to acquire capability. In contrast, because adolescent girls engage in fewer health-risk behaviors, engagement in NSSI during this developmental period may have a particularly profound effect on capability. Future research should continue to explore whether the relation of NSSI to attempted suicide varies by gender, as well as other psychosocial variables such as ethnicity, age, socioeconomic status, and psychiatric diagnosis. Future studies should also examine characteristics of NSSI that most strongly indicate suicide risk; for example, one study found that different NSSI methods, contexts, and functions were differentially related to suicidality (Klonsky & Olino, 2008), and another found that number of NSSI methods used predicted elevated suicidality (Nock et al., 2006).
A key limitation of the present study is the retrospective, cross-sectional design. Establishing the temporal relationship between NSSI and attempted suicide requires prospective research. However, because the onset of NSSI typically occurs around ages 13 or 14 (Klonsky & Muehlenkamp, 2007), which is approximately 10 years earlier than the average onset of attempted suicide (Kessler et al., 1999), we suggest that NSSI typically precedes suicide attempts and may be an especially important predictor of future suicide attempts. Notably, two recent prospective studies of depressed adolescents support our conceptualization: both found that NSSI predicted future suicide attempts more strongly than other suicide risk factors (Asarnow et al., 2011; Wilkinson et al., 2011; for comment see Brent, 2011). Interestingly, both studies also found that suicide attempts were a poor predictor of subsequent NSSI, suggesting that NSSI increases risk for attempted suicide, but attempted suicide does not increase risk for NSSI.
Findings from the present study have important clinical implications. Guidelines for suicide risk assessment often highlight variables such as depression, anxiety, impulsivity, and BPD (American Psychiatric Association, 2006; Rudd et al., 2006). However, NSSI appears to predict attempted suicide more strongly than these risk factors (also see Andover & Gibb, 2010). In addition, NSSI is common in treatment-seeking populations (Briere & Gil, 1998; Nock et al., 2006). Therefore, we recommend that suicide risk assessment guidelines be revised to emphasize NSSI at least as much as other psychological risk factors for suicide.
It is also important that research examine in more detail the relation of NSSI to suicidality. The present study examined suicide attempts as a dichotomous outcome. However, not all suicide attempts are the same. If NSSI increases capability for self-inflicted pain and violence, it is likely that histories of NSSI would facilitate suicide attempts that are more violent, dangerous, and potentially fatal (see Andover & Gibb, 2010). Future research should investigate whether NSSI increases medical severity and lethality of suicide attempts.
A final limitation of the current study is the use of self-report measures of NSSI and suicide attempts. These measures rely on participants' judgments about suicidal intent. Future research utilizing interviews administered by experts can help determine if our findings generalize across different assessment methods.
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Submitted: April 17, 2012 Revised: August 16, 2012 Accepted: August 21, 2012
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Source: Journal of Abnormal Psychology. Vol. 122. (1), Feb, 2013 pp. 231-237)
Accession Number: 2012-27535-001
Digital Object Identifier: 10.1037/a0030278
Record: 54- Title:
- The relationship between session frequency and psychotherapy outcome in a naturalistic setting.
- Authors:
- Erekson, David M.. Department of Psychology, Brigham Young University, Provo, UT, US, david_erekson@byu.edu
Lambert, Michael J.. Department of Psychology, Brigham Young University, Provo, UT, US
Eggett, Dennis L.. Department of Statistics, Brigham Young University, Provo, UT, US - Address:
- Erekson, David M., Counseling and Psychological Services, Brigham Young University, 1500 Wilkinson Student Center, Provo, UT, US, 84602, david_erekson@byu.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 83(6), Dec, 2015. pp. 1097-1107.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 11
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- frequency, psychotherapy, outcome, dose–response, good-enough-level model
- Abstract (English):
- Objective: The dose–response relationship in psychotherapy has been examined extensively, but few studies have included session frequency as a component of psychotherapy 'dose.' Studies that have examined session frequency have indicated that it may affect both the speed and the amount of recovery. No studies were found examining the clinical significance of this construct in a naturalistic setting, which is the aim of the current study. Method: Using an archival database of session-by-session Outcome Questionnaire 45 (OQ-45) measures over 17 years, change trajectories of 21,488 university counseling center clients (54.9% female, 85.0% White, mean age = 22.5) were examined using multilevel modeling, including session frequency at the occasion level. Of these clients, subgroups that attended therapy approximately weekly or fortnightly were compared to each other for differences in speed of recovery (using multilevel Cox regression) and clinically significant change (using multilevel logistic regression). Results: Results indicated that more frequent therapy was associated with steeper recovery curves (Cohen’s f2 = 0.07; an effect size between small and medium). When comparing weekly and fortnightly groups, clinically significant gains were achieved faster for those attending weekly sessions; however, few significant differences were found between groups in total amount of change in therapy. Conclusions: Findings replicated previous session frequency literature and supported a clinically significant effect, where higher session frequency resulted in faster recovery. Session frequency appears to be an impactful component in delivering more efficient psychotherapy, and it is important to consider in individual treatment planning, institutional policy, and future research. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Impact Statement:
- What is the public health significance of this article?—As mental health and the efficiency of mental health treatment have become prominent areas of concern in the broader health care milieu, research on practical constructs that affect clinically significant change have become more important. The current study offers evidence that higher session frequency increases the efficiency of psychotherapy in clinically significant ways, decreasing length of patient suffering and possibly requiring fewer institutional resources. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Psychotherapy; *Treatment Duration; *Treatment Outcomes; Models
- PsycINFO Classification:
- Psychotherapy & Psychotherapeutic Counseling (3310)
- Population:
- Human
Male
Female - Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
- Tests & Measures:
- Outcome Questionnaire-45
- Methodology:
- Empirical Study; Mathematical Model; Quantitative Study
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Oct 5, 2015; Accepted: Aug 14, 2015; Revised: Jul 31, 2015; First Submitted: Dec 6, 2013
- Release Date:
- 20151005
- Correction Date:
- 20160512
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0039774
- PMID:
- 26436645
- Accession Number:
- 2015-45474-001
- Number of Citations in Source:
- 34
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-45474-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2015-45474-001&site=ehost-live">The relationship between session frequency and psychotherapy outcome in a naturalistic setting.</A>
- Database:
- PsycINFO
The Relationship Between Session Frequency and Psychotherapy Outcome in a Naturalistic Setting
By: David M. Erekson
Department of Psychology, Brigham Young University;
Michael J. Lambert
Department of Psychology, Brigham Young University
Dennis L. Eggett
Department of Statistics, Brigham Young University
Acknowledgement: David M. Erekson is now at Counseling and Psychological Services, Brigham Young University.
Michael J. Lambert is a limited liability partner in OQMeasures, the company that owns and distributes the OQ-45 used in this study.
As third-party payers and managed care have become more prominent in managing mental health care, emphasis has been placed on the provision of evidence-based treatments as well as minimizing treatment length and cost (Drake & Latimer, 2012). In psychotherapy, this has led to research attempting to identify an optimal range for the amount of therapy needed for improvement; in other words, to delineate a specific number of sessions that is generally found to be helpful across populations (Hansen, Lambert, & Forman, 2003; Howard, Kopta, Krause, & Orlinsky, 1986). This work has been termed the “dose–response” model of psychotherapy, where each session is a single “dose” that adds to a cumulative “response.” These models have indicated that between 13 and 18 sessions are required for 50% of individuals in therapy to achieve clinically significant change, with diminishing returns for sessions that fall beyond this range (Hansen et al., 2003). An alternative model describing recovery curves in psychotherapy proposed that the number of sessions attended is linearly associated with the speed of recovery, where individuals who attend fewer sessions recover more quickly than those who attend more sessions. This has been termed the “good-enough-level” model, where a client’s rate of recovery determines the number of sessions they receive (rather than the number of sessions determining recovery; Barkham et al., 2006). In both of these models of therapy dose, session frequency, or how often the client is seen in a certain period of time, has largely remained unexamined.
The importance of session frequency can be highlighted by comparing psychotherapy dose–response to a medication model. While medication dosage information includes the number of pills to be taken (analogous to total number of sessions in psychotherapy), it also includes information regarding how often they should be taken (analogous to session frequency). Taking one pill a day for 30 days would presumably have a different effect on the body than taking three pills a day for 10 days, or, alternatively, one pill a week for 8 months. Just as alternate schedules for medication may change the way medication works, session frequency may affect the structure and mechanisms of psychotherapy.
Using available empirical evidence, Orlinsky (2009) outlined a metastructure for psychotherapy across theoretical models (termed the “generic model of psychotherapy”) that can be used to understand the potential impact of session frequency on psychotherapy outcome. The model suggests that all psychotherapies are based on a “therapeutic contract,” which consists of treatment goals, methods, fees, and scheduling, as agreed upon by the client and therapist. This therapeutic contract leads to “therapeutic operations” and the “therapeutic bond.” Therapeutic operations are defined as technical aspects of therapy, which include the client’s presentation of concerns, the therapist’s intervention, and the client’s cooperation with this intervention. The therapeutic bond is the alliance between therapist and client. Together, the contract, operations, and bond are suggested to lead to positive or negative effects from a psychotherapy session that are carried into the client’s life experience and are manifest as an increase or decrease in symptoms.
When considering scheduling specifically (as part of the hierarchically superior therapeutic contract and as affecting attended session frequency), session frequency is theorized to have a direct effect on therapeutic operations and therapeutic bond. The interaction between time between sessions and the effectiveness of therapeutic operations remains unknown, though it is possible that gains may be less likely to add upon each other as the length between sessions increases. This may be supported by behavioral theory, which suggests that continuous reinforcement works best for learning new behaviors, while more attenuated reinforcement schedules (especially early on) work less well; if psychotherapy is conceptualized as reinforcement for more adaptive behaviors, it follows that less learning occurs if time between sessions increases. For example, less continuity in tracking client success and failure with out-of-office assignments may mean that the client misses timely support needed to carry successes forward or to problem-solve failures. Practice-based observations may also support this, where the greater number of events that occur in a 2-week time period may become too many to effectively address in a single session, or where new issues arise that interrupt the continuity of previous in- and out-of-office problem-solving activities.
Infrequent therapy may also attenuate the development and stability of the therapeutic alliance, as a client and therapist may feel less actively involved and connected with each other and the therapy. It seems possible that meeting less frequently may communicate to the client that the therapist is too busy for the client, that the client’s problems are not important to the therapist, or that the therapist does not recognize the client’s suffering. Any of these possibilities would likely interrupt communicated positive regard and communicated empathy, components that are theorized in client-centered therapy to strengthen the therapeutic bond (Erekson & Lambert, 2015). If both therapeutic operations and the therapeutic bond are somewhat impaired, it follows that the psychotherapy would be less efficacious.
Despite its theoretical importance, frequency of psychotherapy sessions has historically rested on a foundation of tradition rather than evidence (e.g., 50-min session delivered weekly). Some oblique support for certain frequencies can be found in evidence-based treatments, where if a treatment has garnered enough empirical support to be termed “evidence-based,” we can assume that the proper delivery of that intervention includes session frequency as outlined in the protocol. In an informal examination of the list of research supported treatments provided by Division 12 of the APA (APA Presidential Task Force, 2006), 47 of the 56 treatments that indicated how frequently sessions were scheduled specified weekly or more frequent sessions for the majority of each intervention, particularly in the early stages of treatment.
More direct support for the importance of session frequency was recently published by Cuijpers, Huibers, Ebert, Koole, and Andersson (2013). In a meta-analysis of 70 randomized trials on individual psychotherapy for adult depression, four variables were examined: (a) total number of sessions, (b) total number of weeks in therapy, (c) intensity of therapy (or the length of each session), and (d) session frequency (as defined by the number of sessions per week). The researchers found minimal effects for the first three variables but a moderate effect size (g = 0.45) for session frequency. Specifically, the study indicated that two sessions per week was significantly more effective in reducing symptoms than a single session per week when treating adult depression.
Session frequency has also been examined in exposure-based treatments for anxiety. Most notably, studies have demonstrated a difference between massed and spaced exposure when treating fear symptoms. The specific parameters of massed or spaced exposure vary from study to study; in general, however, massed exposure indicates an intensive approach to exposure (e.g., several hours in a single day), and spaced exposure indicates exposure sessions that are distributed over a greater period of time (e.g., exposure sessions every 5 days). Massed exposure tends to show better immediate reductions in fear and avoidance behaviors, and spaced exposure tends to show better retention of learning and lower relapse rates (Abramowitz, Foa, & Franklin, 2003; Bohni, Spindler, Arendt, Hougaard, & Rosenberg, 2009; Chambless, 1990; Foa, Jameson, Turner, & Payne, 1980; Rowe & Craske, 1998; Tsao & Craske, 2000). These findings may represent the important impact operations, as conceptualized by Orlinsky (2009), have on treatment outcomes. Though these findings are attenuated by small sample sizes (all fewer than or equal to 40 participants), if applied broadly, they suggest that frequency may affect the amount of recovery in a client and may differentially affect short- and long-term outcomes in psychotherapy.
Cognitive–behavioral therapy research has similarly examined the effect of session frequency on outcome and has found that in addition to affecting the amount of recovery, frequency may affect the speed of recovery. For example, in a comparison of obsessive–compulsive disorder treatment administered either daily (for 14 days) or weekly (for 14 weeks), therapeutic effects seemed to be equally effective, even at a 3-month follow-up (Storch et al., 2008; see also Emmelkamp, van Linden van den Heuvell, Rüphan, & Sanderman, 1989). If these two approaches are indeed equivalent in effect, the more frequent treatment facilitates a faster recovery for the patient. Randomized controlled trials of specific treatments indicate, then, that frequency may have an effect on both the amount of recovery experienced by clients as well as the speed of recovery; this is an indication that warrants examination in a naturalistic setting and with generic nonmanualized treatments, where patients may at times be receiving therapy at protracted frequencies (i.e., once every 2 weeks).
Three studies of session frequency were identified where data were gathered from a working clinic rather than a controlled trial. The first study examined session frequency as the average number of sessions attended each week and included dose (or number of sessions) and duration (or total length of the treatment) as variables in the analysis. The researchers found that neither dose nor duration was a significant predictor of outcome, but fewer sessions and more months of therapy were associated with worse outcomes. Further, they found that higher session frequency for those attending therapy fewer than 5 months was associated with better outcomes, and higher session frequency for those attending therapy more than 5 months was associated with worse outcomes (Reardon, Cukrowicz, Reeves, & Joiner, 2002). These findings may suggest that, when attended more frequently, short-term therapy is more effective and long-term therapy is less effective. However, no control for initial severity of patients’ symptoms hampers interpretation of these results and could reasonably explain the findings independent of session frequency.
The second study examined the association between the number and frequency of sessions within the first 3 months of therapy and final outcome in 256 clients. This association was compared across three theoretical orientations: psychodynamic psychotherapy, cognitive–behavioral therapy, and psychoanalytic psychotherapy. No association was found between initial frequency of sessions and final outcome for psychodynamic and cognitive–behavioral therapies; psychoanalytic therapy, however, tended to have better outcomes when sessions were initially less frequent but regular (Kraft, Puschner, & Kordy, 2006). As with Reardon et al. (2002), there are limitations that prevent extrapolation of these results to the effects of frequency—most notably, significant results applied only to psychoanalytic therapy and not to the other two therapies included in the study.
The third study tracked treatment response at every third session to analyze the change trajectories of 1,207 students seeking counseling at a university counseling center (Reese, Toland, & Hopkins, 2011). The researchers explored whether or not session frequency improved a predictive model of therapy recovery, and they explored the nature of frequency effects if it did improve. Session frequency was defined by subtracting the total number of sessions attended by one and dividing that number by the number of weeks in therapy (yielding a single session frequency average for each individual). They found that session frequency significantly contributed to a multilevel model, independent of the total number of sessions attended. It was also found that higher session frequency (i.e., more sessions in fewer days) was related to faster recovery. Limitations to this study include the outcome measure being given once every third session rather than every session and the operationalization of frequency as a fixed variable for each individual (where session frequency, in fact, can vary over time). Additionally, none of these three studies examined clinical significance of client change.
The current study was designed to address the gaps in the session frequency literature in the following ways. First, when possible, outcome was measured at each session of therapy, allowing for a more complete model of change in therapy. Second, frequency was defined as a dichotomous variable (either approximately once a week or approximately fortnightly) for analyses of clinical significance and as a continuous, time-varying variable for the overall model of change in therapy. The former collapses data into distinct events (e.g., weekly vs. fortnightly, reaching significant change vs. not reaching significant change) and allows for an easily interpreted heuristic that is clinically meaningful. The latter includes all available data and more accurately tracks session frequency and outcome at each point in time (especially when both are variable from session to session), allowing for a more nuanced, if more complex, understanding of the construct. Third, measures of clinical significance, as defined by Jacobson and Truax (1991), were included in order to understand the practical significance of session frequency. Finally, initial severity of symptoms was controlled, allowing for an examination of session frequency effects independent of clients’ distress at intake. Based on the literature above, the following hypotheses were formulated: (1) Session frequency will have a significant effect on the speed of recovery, where more frequent sessions will be related to steeper recovery slopes; (2) session frequency will have a significant effect on the amount of change that occurs in therapy, where more frequent sessions will be related to more clinically significant change and less deterioration.
Method Participants
Archival outcome and appointment data were drawn from the counseling center database of a large western university. University students who received psychotherapy between 1996 and 2014 were included in the database. Therapy at the counseling center was offered free of charge and without session limits to full-time students of the university. Clients were referred or self-referred for a wide range of problems, the majority of which were adjustment, anxiety, or depression related. Individual therapy generally consisted of the traditional 50-min session. Therapists at the counseling center were psychologists or supervised psychologists in training (doctoral students in counseling or clinical psychology) who provide treatment according to their theoretical preference, including cognitive–behavioral, psychodynamic, client-centered, existential, systems, and integrative modalities. Three hundred and three therapists were included in the study, each having seen anywhere between 1 and 673 clients, with the median number of clients being seen by a single therapist equaling 22 and the median number of sessions for a single therapist equaling 122.
Consideration for inclusion in this study as a client participant was restricted to individuals who had only attended individual therapy (i.e., no group treatment; N = 22,235) and who had attended at least two sessions of therapy and completed at least two measures of outcome (allowing for clients to be exposed to a frequency effect and to have a record of that effect). Additionally, we limited the analyses to the first course of therapy for each individual, where a break of 90 days or more was considered a new course of therapy. Finally, we excluded individuals who had attended therapy longer than 40 weeks, as these represented significant outliers (3.4% of the sample), and models including these individuals were unstable. Our final sample included 21,488 students. Of these participants, 54.9% were female and 39.8% were male, with 5.3% unspecified. Their mean age was 22.5. The following reflects the percentages of reported primary ethnicities: 85.0% White, 6.0% Hispanic, 2.5% Asian, 1.2% Pacific Islander, 0.9% American Indian, 0.7% Black, and 3.7% other. The mean number of sessions attended was 5.8 (SD = 4.2), and the mean number of weeks per course of therapy was 9.1 (SD = 8.3).
Identifying Weekly and Fortnightly Frequency Groups
In order to allow for a more clinically meaningful analysis of the speed and amount of change occurring in therapy based on session frequency, we identified two groups: individuals attending approximately weekly therapy and individuals attending approximately fortnightly therapy. This was done by calculating the mean frequency over the entire course of therapy for each individual and selecting those with a mean that fell within .25 of 1 week (the weekly group) or within .25 of 2 weeks (the fortnightly group). Several exploratory procedures with more stringent criteria were used to specify these two groups, but because results based on these procedures were consistent with the above procedure, they are not reported here. The weekly group and the fortnightly group were then randomly matched on age, gender, and initial severity of symptoms in order to better isolate the effects of session frequency. Each group consisted of 3,092 clients, with 60.2% female and 38.9% male and a mean age of 21.84.
Because of the possibility that a therapist may choose to taper the frequency of psychotherapy after an initial, more intense treatment, frequency was additionally calculated for each individual during the first month of therapy (n = 2,934 for each group) and the first 2 months of therapy (n = 1,668 for each group); these frequencies (based on the aforementioned time periods) were then applied to the grouping procedure described above. Analyzing frequencies based on these time periods allowed for examination of the effects of early session frequency on final outcomes, and it acted as a control for possible tapering of sessions toward the end of therapy.
Measures and Procedure
Outcome Questionnaire-45 (OQ-45)
Psychological outcome was assessed during treatment using the OQ-45 (Lambert, Gregersen, & Burlingame, 2004), a 45-item self-report instrument designed to measure client distress and functioning over the last week and typically administered prior to each therapy session to track progress in therapy. Items are rated on a 5-point Likert scale. Total scores can range from 0 to 180, with higher scores reflecting more severe distress and lower scores reflecting less distress.
Previous research has provided evidence for the utility of the OQ-45 as a measure of treatment progress and outcome. The OQ-45 demonstrated an excellent level of internal consistency as calculated in the current sample (α = .93). The manual reports test–retest reliability as .84 over a 3-week period. The OQ-45 is also reported, however, as sensitive to change, improving an average of 17.47 points in a sample of 40 patients receiving psychotherapy. Norms have been established for individuals between the ages of 18 and 80 within university, independent practice, community mental health, outpatient, and inpatient settings (Lambert et al., 2004).
The OQ-45 was administered in the current study either by paper or electronically to patients. Client and therapist were both aware that outcome scores were stored for research purposes, and informed consent was obtained at intake. Although specific refusal rates are unavailable for the current sample (records are removed from the database without being tracked), the majority of students generally agree to participate.
The OQ-45 offers cut-offs for reliable change and clinically significant change, derived from the model of statistically operationalized clinically significant change proposed by Jacobson and Truax (1991). Reliable change is defined as change in observed scores that exceeds the amount of variation expected within the standard error of measurement, which, in the case of the OQ-45, equals at least 14 points. Clinically significant change is distinguished by two criteria: a) the change observed is equal to or exceeds the reliable change index, and b) the score leaves the clinical range of functioning (in the case of the OQ-45, scores >63) and enters the normal range of functioning (OQ-45 ≤ 63). Additionally, a change of 14 points in a negative direction (where the client is worse than when they began) defines reliable deterioration. Reliable change, clinically significant change, and deterioration were all used in the current study to identify meaningful change in therapy.
Variables of interest
We calculated several variables for each case. The variable used to examine session frequency as a continuous variable for all subjects was calculated as a cumulative mean frequency at each session. For example, a person attending therapy 1 week after the first session would receive a 1 for his or her frequency at the second session. If this person’s next session were attended 2 weeks after the second session, he or she would receive a 1.5 mean at the third session; if the next session were 2 weeks after the third, he or she would receive a cumulative mean of 1.67 at the fourth session, and so on. Allowing session frequency to vary with time allows for an accurate representation of the effect of frequency as it is occurring, rather than using a variable that has not yet occurred (e.g., mean frequency over the entire course of therapy) to predict a variable at an earlier point in time (e.g., OQ-45 scores at the second session). Additionally, we calculated the total number of sessions attended and initial severity of symptoms (derived from the first OQ-45 score) to include as covariates. As mentioned previously, the total number of sessions has been shown to be related to recovery slopes in psychotherapy (consistent with the good-enough-level model); initial severity of symptoms, on the other hand, was included as a theoretically important control, where it may be possible that differences in severity contribute to differences in recovery over time (or contribute to differences in frequency). We did not include diagnosis as a variable, despite its theoretical importance, as it was not based on research quality criteria nor consistently recorded in the dataset.
Data Analysis
Speed of recovery
Multilevel modeling (Singer & Willett, 2003; also referred to as hierarchical linear modeling) was used to examine the overall effect of session frequency as a continuous variable on the rate of change in therapy. This statistical method is particularly suited to the data in that it accounts for multiple OQ-45 scores nested within individuals and within therapists. The PROC HPMIXED procedure in SAS, designed for efficient analysis of large numbers of observations and similar to the PROC MIXED procedure, was used to estimate recovery trajectories. The PROC HPMIXED procedure relies on sparse matrix techniques and estimates covariance parameters using restricted maximum likelihood. We used the Bayesian Information Criterion (BIC; Schwarz, 1978) to assess model fit, where a decrease of 10 points in the BIC from one model to the next indicates a significantly better fit to the data (Singer & Willett, 2003). BIC was particularly appropriate for these models, as other assessments of model fit require a maximum likelihood method that is not available in the HPMIXED procedure (and other procedures were unable to manage the large amounts of data in our database). We tested transformations for the time variable, including the linear, quadratic, and cubic transformations based on previous research using OQ-45 trajectories (Baldwin, Berkeljon, Atkins, Olsen, & Nielsen, 2009). All three time variables were found to be significant. However, for simplicity of interpretation of the effect of session frequency over time, and as the largest estimate was linear, models using only linear time were reported here.
The final model used to examine the effects of session frequency was as follows:
where Yjih indicates the OQ-45 score at time j for individual i seeing therapist h. The model includes effects for session (γ200), frequency (γ100), the initial severity of symptoms for each individual (γ010), and the total number of sessions attended (γ020; included to replicate the good-enough-level model of psychotherapy response). The effects for severity and the number of sessions predict differences at the intercept for OQ-45 scores due to these variables. Time by severity (γ210) and time by number of sessions (γ220) interactions were also estimated, as well as the frequency by session (γ300) interaction. These interaction effects predict differences in OQ-45 slope (or rate of change) due to the variables being tested; hence, these were the effects of primary interest in the current study. Random effects are listed last and were included to account for therapist variability around the initial intercept, or potential differences in the initial scores of the clients they treat (v00h), client variability around the initial intercept (u0ih), and client variability in slope (u2ih). Residual error is indicated by ejih.
Using the matched dataset of clients attending weekly and clients attending fortnightly, we assessed the clinical significance of session frequency on speed of recovery using the PROC PHREG procedure in SAS for multilevel Cox regression. We examined differences between groups in rates of reliable change and clinically significant change. Multilevel Cox regression predicts the proportion of subjects who will reach a specified criterion (i.e., reliable change, clinically significant change) by a certain time, while including a clustering variable (i.e., therapist). These analyses were run twice, using weeks in therapy as a time variable (allowing for a comparison between groups over time), and using number of sessions attended as a time variable (allowing for a session-by-session comparison). In other words, weeks-as-time allowed us to examine differences over real time between groups, while sessions-as-time allowed us to compare differences in the effect of each session.
Analyses considering just the first month of session frequency and the first 2 months of session frequency were also performed; results were consistent with the full analyses described above, and details were therefore not included in this report.
Amount of recovery
We used multilevel logistic regression (PROC GLIMMIX procedure in SAS) to explore the effect of session frequency on the amount of recovery experienced in psychotherapy; these analyses also utilized the matched dataset of weekly attending and fortnightly attending clients. Criteria for reliable change, clinically significant change, and deterioration were used as dependent variables; weekly and fortnightly groups entered as independent variables, and therapist entered as a clustering variable. Although the definition for clinically significant change includes having met criteria for reliable change, the variables were defined as nonoverlapping, where a client who met criteria for clinically significant change was not also identified as having met criteria for reliable change. These criteria are therefore independently useful in understanding differences in recovery patterns between groups. These analyses were also run for groups defined by session frequency calculated for the first month of therapy and the first 2 months of therapy.
Results Hypothesis 1: Speed of Recovery
Session frequency as a continuous variable was examined for all subjects (N = 21,488) over the entire course of therapy. The analysis included initial symptom severity and total dose in the model, as described above. Number of sessions in therapy rather than weeks in therapy was used as the time variable in this model in order to best understand the effects of frequency over time without interference from the inherently temporal nature of frequency (e.g., it is difficult to interpret the effects of monthly frequency one week after the first session). First, OQ-45 scores were examined and found to be approximately normally distributed. An intraclass correlation (ICC) examining the amount of variance in OQ-45 scores between subjects and within subjects in a model with no predictors was calculated using the following formula:
where σv2 indicates the variance between subjects and σϵ2 indicates the variance within subjects. The proportion of variance between subjects was .675, indicating that approximately 68% of the variance in OQ-45 scores was attributable to differences between individuals (and that there is variance that may be explained by between-subject predictors).
We then included therapist as a random effect and calculated the 3-level model ICC (Siddiqui, Hedeker, Flay, & Hu, 1996), where the ICC for individuals within therapists was calculated as follows:
where σth2 indicates variance within therapists, σind2 indicates variance between subjects, and σε2 indicates variance within subjects. Approximately 47% of the 3-level model variance in OQ-45 scores was attributable to differences between therapists. This large value is likely due to the nonrandomized design of the study. Between-subject variance in the 3-level model was calculated as follows:
indicating that approximately 73% of the variance was attributable to differences between individuals within therapists.
Missing data
As it is possible that variables included in the model may be systematically associated with missing OQ-45 data, we examined patterns of missing data in the sample. First, we calculated the number of individuals with any missing OQ-45 data. Of 21,488 clients included in the dataset, 4,834 (22.5%) had at least one point of missing data; of these, 3,250 (15.1%) had only a single point missing, and 915 (4.3%) had only two points missing. The remaining 3.1% had between 3 and 35 points of missing data.
We then used Little’s MCAR test to determine if any significant patterns of missing data occurred, or if data were missing completely at random; the null hypothesis for this test is that missing data are distributed randomly. Little’s MCAR resulted in a χ2 of 11144.85 (df = 6, p < .001), indicating that missing data appear to be related to session frequency (EM Correlation = .005), initial severity (EM Correlation = .73), and total number of sessions attended (EM Correlation = .05). In order to better understand these relationships and decrease the possibility of these correlations biasing results, we defined all possible missing data patterns for individuals who had received up to eight sessions of therapy (Hedeker & Gibbons, 1997). For example, if a person attended three sessions (and given that individuals were selected if they had an initial OQ and at least one other OQ measurement), we defined two patterns: missing OQ data for the second session or missing OQ data for the third session. Six patterns were defined for those who attended four sessions, 14 patterns for those who attended five sessions, and so on, with 126 possible patterns for those who attended eight sessions. Each of these patterns was then dummy coded and included as a class variable in the multilevel model; the effects of the variables of interest included in the model (described below) remained unchanged. We examined differences in these patterns using one-way ANOVAS but found no discernible pattern in the significant differences, where disparate patterns of missingness were associated with both high and low levels of initial severity, total number of sessions, and session frequency. In order to further assess the effects of correlated missing data, we ran a model for only those with no missing data, and we ran a model for only those with missing data. Both analyses yielded similar effects regarding the variables of interest; in order to limit bias in the results related to missing data, however, we ran the final model including only individuals with no missing data (n = 16,654).
Linear model
Table 1 presents a linear model estimating fixed and random effects. The intercept estimate indicates an average initial OQ-45 score of 70.64. As expected, initial severity and total dose yielded significant effects on recovery curves. Higher levels of initial severity were found to be significantly associated with a higher OQ-45 score at the first session after initial measurement, as well as steeper slopes of recovery. Higher levels of total dose were associated with lower levels of OQ-45 scores at intercept and with less steep recovery curves, consistent with the good-enough level model.
Multilevel Model Predicting the Effects of Session Frequency on Change Trajectories
Including session frequency in the model significantly improved model fit (a decrease of 8,515 in BIC, where a decrease of at least 10 indicates significance). Session frequency was also significantly associated with the intercept and slope of the model, indicating lower OQ-45 scores at intercept and less steep recovery curves with less frequent therapy. This effect can be practically interpreted through extrapolation of the estimate as it interacts with (or is multiplied by) the time variable. In other words, a decrease of 1 week in session frequency has a different impact at different points in time. For example, the slope of OQ-45 scores at Session 2 for a person attending weekly would be estimated as −2.4, and −2.0 for a person attending monthly. At Session 6, however, the estimate would be −1.9 for the weekly client and 0.3 for the monthly client. Cohen’s ƒ2 was used to calculate an effect size based on the amount of variance explained by the variable; this statistic has been discussed as being particularly suitable for use with multilevel models (Selya, Rose, Dierker, Hedeker, & Mermelstein, 2012). Guidelines for interpretation of this statistic have been outlined by Cohen (1988), where ƒ2 ≥ 0.02 is considered a small effect, ƒ2 ≥ 0.15 is considered a medium effect, and ƒ2 ≥ 0.35 is considered a large effect. The effect size for session frequency was calculated as 0.07, or between a small and medium effect. Figure 1 illustrates the isolated effects of this interaction (accounting for the effects of initial severity and total dose). Each line represents an aggregate recovery slope based on a different session frequency. In order to better illustrate the effects of frequency over time, each line contains an equal number of sessions (i.e., six, or the mean number of sessions). As can be seen, sessions attended once a week have the steepest recovery slopes, with progressively less steep slopes occurring for those being seen fortnightly, every 3 weeks, or once a month.
Figure 1. The unique effect of session frequency on OQ-45 recovery slopes based on multilevel modeling. Note. White circles represent a single session. Slopes represent the effect of different session frequencies at Session 6, controlling for initial severity and total number of sessions, as specified by multilevel modeling.
Analyses of clinically significant change
Samples matched on age, gender, and initial severity were compared using multilevel Cox regression with therapist as a clustering variable. Both groups had a mean initial OQ-45 of 68.14. The weekly group had a mean final OQ-45 of 57.64, and they attended a mean of 4.80 (SD = 3.59) sessions over a mean of 4.1 (SD = 4.15) weeks. The fortnightly group had a final OQ-45 of 57.62 and a mean of 6.68 (SD = 4.62) sessions over a mean of 11.2 (SD = 9.11) weeks.
The first analysis of these groups compared rates of reliable change by session number. Significant differences were found between the weekly and fortnightly groups (χ2 = 75.65, df = 1, p < .001, hazard ratio = 1.35, 95% CI [1.27, 1.45]), where more individuals were predicted to reach reliable change sooner in the weekly group. The second analysis compared clinically significant change by session number. The same pattern observed in the previous analysis emerged for clinically significant change, where the weekly group met criteria significantly sooner than the fortnightly group (χ2 = 39.36, df = 1, p < .001, hazard ratio = 1.36, 95% CI [1.24, 1.50]).
The third analysis examined the rate of reliable change by weeks in therapy. Again, differences between weekly and fortnightly groups were significant, favoring the weekly group (χ2 = 600.47, df = 1, p < .001, hazard ratio = 2.45, 95% CI [2.28, 2.63]). The fourth analysis, or rate of clinically significant change by weeks, found results consistent with previous analyses (χ2 = 334.89, df = 1, p < .001, hazard ratio = 2.58, 95% CI [2.33, 2.85]). Differences between weekly and fortnightly groups are illustrated in Figure 2.
Figure 2. Clinically meaningful differences in recovery of clients attending therapy weekly (blue) versus fortnightly (red) in Cox regression plots with 95% confidence bands. Note. Graphs depicting recovery proportions by sessions provide a session-by-session comparison of the effect of weekly therapy versus fortnightly therapy (e.g., 5 sessions of weekly therapy vs. 5 sessions of fortnightly therapy). Graphs based on weeks show how these differences unfold in real time, where clients in the fortnightly group are receiving approximately half the number of sessions as the weekly group.
Hypothesis 2: Amount of Recovery
Multilevel logistic regressions were calculated to predict reliable change, clinically significant change, and deterioration based on weekly or fortnightly therapy, with therapist entered as a clustering variable. Groups selected based on session frequency over the entire course of therapy showed no significant prediction of reliable change or clinically significant change. Deterioration, however, was predicted to occur significantly more frequently in the fortnightly group (F(1, 57) = 7.63, p < .001, OR = 1.40, 95% CI [1.10, 1.79]). Actual rates of deterioration were 6.3% for the weekly group and 8.9% for the fortnightly group, consistent with the calculated odds ratio, where an individual attending fortnightly would be 1.4 times more likely to deteriorate than an individual attending weekly.
In order to assess for the effects of early session frequency patterns, groups based on the first month of session frequency and on the first 2 months of session frequency were examined. When selected by just the first month of session frequency, both groups had a mean initial OQ-45 of 66.66. The weekly group had a mean final OQ-45 of 56.89, attended a mean of 5.0 (SD = 3.78) sessions, for a mean of 8.7 (SD = 6.70) weeks. The fortnightly group had a mean final OQ-45 of 58.22, and a mean of 7.2 (SD = 4.61) sessions over a mean of 13.5 (SD = 8.37) weeks.
When selected by just the first 2 months of session frequency, both groups had a mean initial OQ-45 of 70.57. The weekly group had a mean final OQ-45 of 55.90 and attended a mean of 6.5 (SD = 3.76) sessions over a mean of 8.6 (SD = 4.37) weeks. The fortnightly group had a mean final OQ-45 of 57.39 and a mean of 5.4 (SD = 3.90) sessions over a mean of 9.4 (SD = 7.79) weeks.
Groups selected by assessing just the first month of session frequency showed significant differences in clinically significant change (F(1, 52) = 7.04, p = .01, OR = 1.23, 95% CI [1.05, 1.44]) and no other significant results. Groups selected by assessing the first 2 months of session frequency had no significant results. Full results for these analyses can be seen in Table 2.
Clinically Significant Differences in Total Recovery Between Weekly and Fortnightly Groups
DiscussionThis archival study examined the effect of different session frequencies on psychotherapy change trajectories in a routine-care counseling center. Previous dose–response literature in psychotherapy has focused on the total number of sessions required for improvement, or on the speed of change in therapy predicting the total number of sessions (the good-enough level model; Barkham et al., 2006), while largely neglecting session frequency as a component in the dose–response model. Additionally, recent discussions have emphasized the importance of reducing the burden of mental illness and the implications of better implementing empirically supported treatments, including delivery of these treatments on a weekly or more frequent basis (Cuijpers et al., 2013; Kazdin & Blase, 2011). As the bulk of empirically supported treatments have either explicitly or implicitly employed structured session frequencies of once a week or more frequently, shifting from this practice in routine-care settings may negatively affect the efficacy of treatment. This effect is predicted by Orlinsky’s (2009) generic model of psychotherapy and is empirically supported in the current analysis.
The archival data used in the current study and absence of experimental controls necessitated the use of multiple statistical methods in order to attempt to isolate the effects of session frequency. For example, session frequency was defined as a continuous variable for multilevel models and as a dichotomous grouping variable for analyses of clinically significant change. While the first has the advantage of using the full range of data, the second has the advantage of being a clinically useful heuristic. Additionally, numerous methods of “control” were employed, including matching samples, covariates, and time-period analyses. Although these add to the complexity of the study, they also increase confidence that the effects found are attributable to the variables to which we are assigning them.
When considering the effect of session frequency on the speed of recovery in therapy, two techniques were used: multilevel modeling with session frequency as a continuous variable, and comparing weekly and fortnightly groups using clinically meaningful criteria. Multilevel modeling indicated that those seen more frequently were estimated to recover faster. The effect size for this finding falls between small and moderate.
It is important to note that this effect was found in a model using sessions as time. If one conceptualizes the efficacy of a session as the amount of change occurring during or after that session, this model allowed us to identify decreased efficacy in sessions occurring less frequently. This is in contrast to the alternative hypothesis that sessions have equal efficacy and that equal change would occur between two sessions whether they occurred weekly or less frequently. Practically, this effect indicates that the cumulative effect of how frequently a client is seen matters at each session and that seeing that client more frequently may lead to faster recovery.
This analysis also indicated that session frequency is an important component of the dose–response model. The current study was consistent with the good enough level model, where fewer total sessions in therapy were significantly associated with steeper recovery slopes (Baldwin et al., 2009; Barkham et al., 2006); the good enough level model fit was even better, as indicated by the BIC, when including session frequency.
Clinically meaningful differences in the speed of recovery were examined using weekly and fortnightly groups as proxy for session frequency. These analyses supported the effects of session frequency found in the multilevel model, where weekly therapy attained a higher proportion of reliable change and clinically significant change than fortnightly therapy. This effect was found using sessions as time and weeks as time. For example, the analyses predicted that 50% of individuals being seen weekly would reach reliable change in approximately eight sessions, while those being seen fortnightly would need approximately 12 sessions. Analyses using weeks as time predicted 50% of the weekly group reaching reliable change in approximately 6 weeks, with the fortnightly group requiring 21 weeks of treatment. This highlights, again, the decrease in session-to-session efficacy in less frequent therapy.
When considering the effect of session frequency on the amount of change achieved by the end of therapy, findings were less clear, and our hypothesis was not supported. Reliable change and clinically significant change appear to be equally likely in both weekly and fortnightly modalities. In combination with previous findings, this appears to indicate that although fortnightly therapy may require more sessions, it will eventually result in equal levels of recovery by the end of therapy. A similar pattern can be seen in Figure 2, where confidence bands for session-by-session comparisons of the two modalities begin to overlap as the number of sessions increases. The two significant results found in the analyses of total amount of change indicated greater recovery in the weekly group and greater deterioration in the fortnightly group, but these differences were small and anomalous; further research may be warranted, however, in order to either replicate or better understand these results. Across all analyses, it appears that for individuals who are undergoing psychotherapy in a counseling-center setting, routine treatment on a weekly basis is superior to treatment received less frequently in terms of speed of recovery.
It is beyond the scope of this study to identify the mechanism of these findings, but there are several possibilities that may be fruitful for further study. We noted in the Introduction that Orlinsky’s (2009) generic model of psychotherapy posits that frequency of sessions matter and can affect both the quality of the therapeutic alliance as well as the effectiveness of specific therapeutic operations. We propose that this is a reasonable working model to explain the effects of session frequency in the current study. When placed in a practical context, it seems logical that more frequent therapeutic contact for a patient receiving, for example, exposure therapy, would be more effective than a less frequent schedule, as the patient may benefit from the added support to engage in behaviors to which they feel averse. One can imagine that session frequency would also be important for therapies that are less aversive, as clients that are able to meet weekly may have a more intensive and perhaps intense therapeutic encounter that assures greater continuity in therapeutic operations. As mentioned previously, clients may also feel that their needs matter more to therapists who schedule more frequently, believing that the therapist is being responsive to their suffering instead of to the administrative needs of the institution. Of course, these conjectures are in need of empirical confirmation. A first step may be to examine variables known to correlate with positive outcomes such as the therapeutic alliance (Horvath, Del Re, Flückiger, & Symonds, 2011) or positive engagement.
There were several limitations to the current study, including a lack of research quality diagnostic information for patients, which did not allow an investigation of differences in frequency patterns for differing problems and could not address the findings of Cuijpers et al.’s (2013) meta-analysis of depression-specific outcomes, including therapy offered more frequently than once weekly. It is possible that certain diagnostic categories are associated with success at different frequencies. There are also many problems that a global measure of outcome (the OQ-45) may not detect, and we were unable to examine how specific problems may be affected by session frequency.
The pattern of missing data, which was not missing completely at random, also presents a limitation to the current study. Although we investigated multiple patterns of missingness and found no effect of these patterns on the variables of interest, we ultimately chose to delete missing data list-wise. This in itself may bias results (given that those with missing data may differ fundamentally from those without); however, our analysis comparing those with missing data to those without, as well as the relatively small percentage of individuals with missing data, allow for more confidence in the current findings. Also, because clients were not randomly assigned to strictly controlled frequency groups but were seen at intervals that may have been affected by multiple factors (e.g., client’s schedule, therapist’s schedule, semester shifts, and life events), it was more difficult to isolate session frequency. Although efforts were made to manage these difficulties (described above), an experimental design could more accurately isolate these effects.
In addition to difficulty isolating the effects of frequency, there are also difficulties that arise in the heterogeneity of individuals identified by their attended session frequency versus their scheduled session frequency. For example, when defined by attendance, a client scheduled fortnightly that attended fortnightly would have been grouped with a client scheduled weekly who, due to cancellations or no shows, attended only fortnightly. One could imagine that these two clients would differ dramatically in their final outcomes and rates of change. Similarly, if defined by scheduling, attendance may have varied widely between those grouped together (e.g., equating an individual scheduled weekly for 6 weeks who attended six sessions with an individual scheduled weekly for 6 weeks who attended only two sessions). These differences were explored empirically in a less current dataset using a variable defined as the ratio of sessions attended to sessions scheduled, and the interested reader is directed to the original dissertation manuscript (Erekson, 2013). Ultimately, a prospective study would be needed in order to fully address this issue.
Other limitations include generalizability to populations outside of university counseling centers or to more diverse centers, and further research is needed with other populations and in other settings to confirm these effects. Additionally, given the behavioral literature regarding better retention and decreased relapse rates for spaced exposure, this study was unable to address either retention or durability of outcomes associated with different frequencies, as the archival database did not include this information. Finally, the session frequency effect was relatively small (Cohen’s f2 = .07); small effects can, however, significantly impact large populations over extended periods of time. This is true when considering widespread practices of psychotherapy delivery.
Despite these limitations, this study replicated the effects found by previous session frequency studies, although the literature in this area is sparse (Cuijpers et al., 2013; Reardon et al., 2002; Reese et al., 2011). It also contributed several important factors. First, this study used measurements at each session to better predict outcome trajectories. Second, session frequency was defined as a time varying variable rather than a fixed variable, more accurately representing session frequency at each point of measurement and avoiding using an event that has not yet occurred to predict an earlier data point. Third, this study moved beyond statistical significance and offered an examination of the clinical significance of session frequency, thus providing a practical and concrete estimate of impact. Finally, the study was designed to be clinically accessible by comparing simple attendance patterns of once a week versus once every 2 weeks. Although it could not study all possible real-world practices, it allowed for a useful heuristic that might guide typical psychotherapy scheduling.
Evidence from past literature and the current study indicates that session frequency affects the speed of recovery in psychological treatment. It remains unclear, organizationally and in the community, if slower recovery for many is better than faster recovery for fewer. A cost–benefit analysis of these approaches would be needed to address the financial effects of these practices, though it appears that, at the very least, less frequent therapy eventually requires more resources to reach the same outcome as more frequent therapy. There are also, however, significant individual and societal costs associated with prolonging negative mental health states. Poor mental health has been associated with decreased household income (Sareen, Afifi, McMillan, & Asmundson, 2011), decreased productivity and increased health care costs (Goetzel, Ozminkowski, Sederer, & Mark, 2002), and increased mortality (Eaton et al., 2008). The Global Disability Index, a measure of overall burden of illness that can be applied across diagnoses, ranked moderate depression as equivalent with multiple sclerosis or deafness and severe depression as equivalent to blindness (Eaton et al., 2008). If attenuating session frequency decreases speed of recovery, it also increases the burden of illness, both societally and in the personal suffering of those receiving psychotherapy. Further research is needed, but there is gathering evidence (including the current study) that more frequent therapy is more effective therapy.
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Submitted: December 6, 2013 Revised: July 31, 2015 Accepted: August 14, 2015
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Source: Journal of Consulting and Clinical Psychology. Vol. 83. (6), Dec, 2015 pp. 1097-1107)
Accession Number: 2015-45474-001
Digital Object Identifier: 10.1037/a0039774
Record: 55- Title:
- Treatment engagement and response to CBT among Latinos with anxiety disorders in primary care.
- Authors:
- Chavira, Denise A.. Department of Psychology, University of California-Los Angeles, Los Angeles, CA, US, dchavira@psych.ucla.edu
Golinelli, Daniela, ORCID 0000-0002-6433-1752. RAND Corporation, Santa Monica, CA, US
Sherbourne, Cathy. RAND Corporation, Santa Monica, CA, US
Stein, Murray B.. Department of Psychiatry, University of California-San Diego, San Diego, CA, US
Sullivan, Greer. Department of Psychiatry, University of Arkansas for Medical Sciences, AR, US
Bystritsky, Alexander. Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA, US
Rose, Raphael D.. Department of Psychology, University of California-Los Angeles, Los Angeles, CA, US
Lang, Ariel J.. Veterans Affairs San Diego Health Care System Center of Excellence for Stress and Mental Health, San Diego, CA, US
Campbell-Sills, Laura. Department of Psychiatry, University of California-San Diego, San Diego, CA, US
Welch, Stacy. Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, WA, US
Bumgardner, Kristin. Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, WA, US
Glenn, Daniel. Department of Psychology, University of California-Los Angeles, Los Angeles, CA, US
Barrios, Velma. Los Angeles County Department of Mental Health, Los Angeles, CA, US
Roy-Byrne, Peter. Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, WA, US
Craske, Michelle. Department of Psychology, University of California-Los Angeles, Los Angeles, CA, US - Address:
- Chavira, Denise A., Department of Psychology, University of California-Los Angeles, 1285 Franz Hall, Box 951563, Los Angeles, CA, US, 90095-1563, dchavira@psych.ucla.edu
- Source:
- Journal of Consulting and Clinical Psychology, Vol 82(3), Jun, 2014. pp. 392-403.
- NLM Title Abbreviation:
- J Consult Clin Psychol
- Page Count:
- 12
- Publisher:
- US : American Psychological Association
- Other Journal Titles:
- Journal of Consulting Psychology
- Other Publishers:
- US : American Association for Applied Psychology
US : Dentan Printing Company
US : Science Press Printing Company - ISSN:
- 0022-006X (Print)
1939-2117 (Electronic) - Language:
- English
- Keywords:
- Latinos, anxiety, engagement, primary care, treatment, cognitive behavioral therapy, treatment outcome
- Abstract:
- Objective: In the current study, we compared measures of treatment outcome and engagement for Latino and non-Latino White patients receiving a cognitive behavioral therapy (CBT) program delivered in primary care. Method: Participants were 18–65 years old and recruited from 17 clinics at 4 different sites to participate in a randomized controlled trial for anxiety disorders, which compared the Coordinated Anxiety Learning and Management (CALM) intervention (consisting of CBT, medication, or both) with usual care. Of those participants who were randomized to the intervention arm and selected CBT (either alone or in combination with medication), 85 were Latino and 251 were non-Latino White; the majority of the Latino participants received the CBT intervention in English (n = 77). Blinded assessments of clinical improvement and functioning were administered at baseline and at 6, 12, and 18 months after baseline. Measures of engagement, including attendance, homework adherence, understanding of CBT principles, and commitment to treatment, were assessed weekly during the CBT intervention. Results: Findings from propensity-weighted linear and logistic regression models revealed no statistically significant differences between Latinos and non-Latino Whites on symptom measures of clinical improvement and functioning at almost all time points. There were significant differences on 2 of 7 engagement outcomes, namely, number of sessions attended and patients’ understanding of CBT principles. Conclusions: These findings suggest that CBT can be an effective treatment approach for Latinos who are primarily English speaking and likely more acculturated, although continued attention should be directed toward engaging Latinos in such interventions. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Document Type:
- Journal Article
- Subjects:
- *Anxiety Disorders; *Cognitive Behavior Therapy; *Primary Health Care; *Treatment Outcomes; *Latinos/Latinas
- Medical Subject Headings (MeSH):
- Adult; Aged; Anxiety; Anxiety Disorders; Cognitive Therapy; European Continental Ancestry Group; Female; Hispanic Americans; Humans; Language; Linear Models; Logistic Models; Male; Middle Aged; Primary Health Care; Treatment Outcome
- PsycINFO Classification:
- Health & Mental Health Treatment & Prevention (3300)
- Population:
- Human
- Location:
- US
- Age Group:
- Adulthood (18 yrs & older)
Young Adulthood (18-29 yrs)
Thirties (30-39 yrs)
Middle Age (40-64 yrs)
Aged (65 yrs & older) - Tests & Measures:
- Overall Anxiety Severity and Impairment Scale
Sheehan Disability Scale
Short-Form Health Survey
Mental Health Composite Summary Scale
Mental Health Composite Scale
Brief Symptom Inventory DOI: 10.1037/t00789-000
Anxiety Sensitivity Index DOI: 10.1037/t00033-000
Mini International Neuropsychiatric Interview DOI: 10.1037/t18597-000
Patient Health Questionnaire-9 DOI: 10.1037/t06165-000 - Grant Sponsorship:
- Sponsor: National Institute of Mental Health
Grant Number: U01 MH057858
Recipients: Roy-Byrne, Peter
Sponsor: National Institute of Mental Health
Grant Number: U01 MH058915
Recipients: Craske, Michelle
Sponsor: National Institute of Mental Health
Grant Number: U01 MH 070022
Recipients: Sullivan, Greer
Sponsor: National Institute of Mental Health
Grant Number: U01 MH057835 and K24 MH64122
Recipients: Stein, Murray B.
Sponsor: National Institute of Mental Health
Grant Number: K01 MH072952
Recipients: Chavira, Denise A. - Methodology:
- Empirical Study; Quantitative Study; Treatment Outcome
- Format Covered:
- Electronic
- Publication Type:
- Journal; Peer Reviewed Journal
- Publication History:
- First Posted: Mar 24, 2014; Accepted: Jun 21, 2013; Revised: Jun 19, 2013; First Submitted: Mar 30, 2012
- Release Date:
- 20140324
- Correction Date:
- 20140519
- Copyright:
- American Psychological Association. 2014
- Digital Object Identifier:
- http://dx.doi.org.offcampus.lib.washington.edu/10.1037/a0036365
- PMID:
- 24660674
- Accession Number:
- 2014-10290-001
- Number of Citations in Source:
- 95
- Persistent link to this record (Permalink):
- http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-10290-001&site=ehost-live
- Cut and Paste:
- <A href="http://offcampus.lib.washington.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2014-10290-001&site=ehost-live">Treatment engagement and response to CBT among Latinos with anxiety disorders in primary care.</A>
- Database:
- PsycINFO
Treatment Engagement and Response to CBT Among Latinos With Anxiety Disorders in Primary Care
By: Denise A. Chavira
Department of Psychology, University of California, Los Angeles, and Department of Psychiatry, University of California, San Diego;
Daniela Golinelli
RAND Corporation, Santa Monica, California
Cathy Sherbourne
RAND Corporation, Santa Monica, California
Murray B. Stein
Department of Psychiatry and Department of Family and Preventive Medicine, University of California, San Diego
Greer Sullivan
Department of Psychiatry and South Central Mental Illness Research, Education, and Clinical Center, University of Arkansas for Medical Sciences
Alexander Bystritsky
Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles
Raphael D. Rose
Department of Psychology, University of California, Los Angeles
Ariel J. Lang
Veterans Affairs San Diego Health Care System Center of Excellence for Stress and Mental Health, San Diego, California, and Department of Psychiatry, University of California, San Diego
Laura Campbell-Sills
Department of Psychiatry, University of California, San Diego
Stacy Welch
Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, and Harborview Center for Healthcare Improvement for Addictions, Mental Illness, and Medically Vulnerable Populations (CHAMMP), Seattle, Washington
Kristin Bumgardner
Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, and Harborview Center for Healthcare Improvement for Addictions, Mental Illness, and Medically Vulnerable Populations (CHAMMP), Seattle, Washington
Daniel Glenn
Department of Psychology, University of California, Los Angeles
Velma Barrios
Los Angeles County Department of Mental Health, Los Angles, California
Peter Roy-Byrne
Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, and Harborview Center for Healthcare Improvement for Addictions, Mental Illness, and Medically Vulnerable Populations (CHAMMP), Seattle, Washington
Michelle Craske
Department of Psychology, University of California, Los Angeles
Acknowledgement: This work was supported by Grants U01 MH057858 (Peter Roy-Byrne), U01 MH058915 (Michelle Craske), U01 MH 070022 (Greer Sullivan), U01 MH057835 and K24 MH64122 (Murray B. Stein), and K01 MH072952 (Denise A. Chavira) from the National Institute of Mental Health. The authors wish to thank Jeanne Miranda for conducting the cultural competency training with therapists in this study.
Anxiety disorders are prevalent in Latino populations. Findings from epidemiological studies based in the United States suggest that lifetime rates of anxiety disorders among Latinos range from 19% to 30% (Burnam, Hough, Karno, Escobar, & Telles, 1987; Vega et al., 1998; Vincente et al., 2006). Data also suggest that U.S.-born Latinos, particularly those of Mexican origin, are at higher risk than immigrant Latinos, for mood, anxiety, and substance use disorders (Alegría et al., 2008; Grant et al., 2004; Vega et al., 1998). Despite the remarkable presence of anxiety and related disorders, Latinos are less likely than non-Latino Whites to use outpatient mental health services (Miranda & Green, 1999; Ojeda & McGuire, 2006) and are also less likely to receive evidence-based care (U.S. Department of Health and Human Services, 2001). These disparities underscore a treatment need for a large and growing segment of the U.S. population.
Cognitive behavioral therapy (CBT) is the primary evidence-based psychosocial intervention for anxiety disorders (Butler, Chapman, Forman, & Beck, 2006). However, few studies have examined the efficacy of CBT with Latinos (Miranda et al., 2005; U.S. Department of Health and Human Services, 2001). Most randomized controlled trials (RCTs) for adults with anxiety disorders have only recruited small proportions of Latinos, making any kind of ethnic specific analysis impossible. As an example, in a recent review of RCTs for obsessive–compulsive disorder, only 1.0% of 2,221 participants from 21 trials were Hispanic/Latino (Williams, Powers, Yun, & Foa, 2010). Studies examining the efficacy of CBT for Latino children and adolescents with anxiety disorders are more common, albeit still few. Findings from these RCTs have found comparable outcomes among Latino and non-Latino White youth on measures of clinical response, remission, symptom severity, and overall functioning (Piña, Silverman, Fuentes, Kurtines, & Weems, 2003; Piña, Zerr, Villalta, & Gonzales, 2012; Silverman, Piña, & Viswesvaran, 2008).
RCTs that evaluate CBT for Latinos with depression, a distinct but related disorder, are more numerous, particularly in primary care settings (Horrell, 2008; Miranda et al., 2005). Primary care–based interventions may be particularly well suited for Latinos who often experience more barriers to access and endorse more stigma regarding seeking services from specialty mental health settings (Vega et al., 2007; Vega & Lopez, 2001). Findings from these studies suggest that Latinos with depression, including low-income and Spanish-speaking patients, have responses to CBT comparable to those of other ethnic groups (Miranda, Azócar, Organista, Dwyer, & Areane, 2003; Muñoz et al., 1995). In large-scale quality improvement programs, which have included a CBT option, significant short- and long-term effects have been found for quality of care received by Latinos, African Americans, and non-Latino Whites, and significant reductions in depressive symptoms have also been found for Latinos and African Americans (Miranda, Duan, et al., 2003; Wells et al., 2005). In smaller, community-based studies, favorable responses to CBT have also been found for various Latino ethnic subgroups (Comas-Díaz, 1981; Organista, Muñoz, & Gonzalez, 1994; Rosselló & Bernal, 1999).
While findings offer some support for comparable clinical outcomes among Latino and non-Latino White participants who have received CBT, less attention has been devoted to engagement-related constructs, which typically reflect the extent to which a patient participates in treatment (e.g., treatment uptake, adherence, and attrition). Previous studies have found that lower engagement, as defined by fewer sessions attended, less homework adherence, or higher rates of attrition, can have negative effects on clinical outcomes (Glenn et al., 2013; O’Brien, Fahmy, & Singh, 2009). Studies that have examined differences in engagement between Latinos and non-Latino Whites have mostly focused on depression and have shown higher attrition rates in both pharmacological and psychosocial interventions for Latinos than for non-Latino Whites (Arnow et al., 2007; Organista et al., 1994). Additionally, problems with medication compliance, CBT attendance, and completion of CBT homework assignments among Latinos have been reported (Aguilera, Garza, & Muñoz, 2010; Ayalon, Areán, & Alvidrez, 2005; Miranda & Cooper, 2004). To our knowledge, no studies have examined engagement outcomes for Latino adults participating in a CBT intervention for anxiety disorders.
The current study addresses a gap in the literature on the impact of culture and ethnicity on treatment outcomes for patients with anxiety disorders. In this study, participants were recruited from primary care settings and received therapist-delivered, computer-assisted CBT for anxiety disorders (CALM: Tools for Living) as part of the CALM (Coordinated Anxiety Learning and Management) study (Craske, Rose, et al., 2009; Roy-Byrne et al., 2010). Clinical outcomes such as symptom reduction and remission as well as engagement-related outcomes including session attendance, treatment completion, homework adherence, and acceptance of CBT, were examined. Based on the available literature, we hypothesized that Latinos who received CBT would have similar clinical outcomes as non-Latino Whites at the various assessment points. We also hypothesized that engagement outcomes would be less favorable among Latino compared with non-Latino Whites; however, given the limited literature, analyses were somewhat exploratory in this regard.
Method Participants
Over a 2-year recruitment period, 1,004 patients with anxiety disorders were recruited from a total of 17 primary care clinics for participation in the CALM study (for a full description, see Roy-Byrne et al., 2010; Sullivan et al., 2007). Study clinics were located in Little Rock, Arkansas, Los Angeles and San Diego, California, and Seattle, Washington. Prior to start of the study, all primary care professionals were educated about the CALM program and eligibility criteria. All recruitment was facilitated by primary care providers who had the option to use a brief anxiety screener (Means-Christensen, Sherbourne, Roy-Byrne, Craske, & Stein, 2006) or to refer patients directly to the study.
All patients had to be between the ages of 18 and 75 years and meet Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev., or DSM–IV–TR; American Psychiatric Association, 2000) criteria for one or more of the following: anxiety disorders; panic disorder (PD), generalized anxiety disorder (GAD), social anxiety disorder (SAD), and posttraumatic stress disorder (PTSD). The Mini International Neuropsychiatric Interview (Sheehan et al., 1998) was used to determine diagnostic eligibility. Patients also had to score 8 or above on the Overall Anxiety Severity and Impairment Scale (OASIS; Campbell-Sills et al., 2009) to ensure at least moderate anxiety-related symptoms and impairment on this validated quantitative measure. Comorbidity was permitted, including major depressive disorder, alcohol abuse, nicotine dependence, and marijuana abuse. Individuals who had other conditions that would compromise their participation in the program or who were unlikely to benefit from CALM were excluded (e.g., unstable medical conditions, marked cognitive impairment, active suicidal intent or plan, psychosis, bipolar I disorder, and substance use disorders except for nicotine dependence, alcohol abuse, and marijuana abuse). Patients already receiving CBT or medication from a psychiatrist in the community were excluded, as were persons who could not speak and read in English or Spanish. All patients gave written informed consent for the study, which was approved by each institution’s institutional review board.
Procedure
After the initial eligibility interview with an anxiety clinical specialist (an ACS, a clinician trained to facilitate the CALM intervention), patients were randomized to intervention or usual care (UC), using an automated computer program at RAND Corporation. All assessments after the initial eligibility interview were conducted by telephone in English or Spanish by members of the RAND Survey Research Group, who were blind to treatment assignment. Randomization was stratified by clinic and presence of comorbid major depression using a permuted block design.
Patients in the intervention group were initially allowed to choose which treatment they wanted to receive—medication, CBT, or both—for 12 weeks. Clinicians asked patients with more than one anxiety disorder who received CBT to choose the most disabling or distressing disorder to focus on, with the expectation that treatment effects would generalize to their other disorders. CBT was administered by the ACS; medication was prescribed by the primary care provider, with consultation from study psychiatrists as needed. A computer program (CALM Tools for Living) was used to assist with the delivery of the CALM intervention; this program was used as an adjunctive tool and not as a stand-alone intervention. Overall, the therapist-delivered CBT program included five generic modules (education, self-monitoring, hierarchy development, breathing training, and relapse prevention) and three modules tailored to the four specific anxiety disorders (cognitive restructuring, exposure to internal stimuli, and exposure to external stimuli; see Craske, Rose, et al., 2009). All intervention materials were translated into Spanish by certified translators, including the computer program.
For intervention patients who opted for medication management, the ACS monitored adherence to the medication regimen and provided basic counseling to encourage healthy behaviors (e.g., avoidance of alcohol and improvement of sleep hygiene and behavioral activation). The ACS also conveyed pharmacotherapy suggestions from the supervising psychiatrist to the primary care physician.
A total of 14 ACSs were involved in this project and administered the eligibility assessment and CALM intervention. The ACSs included six social workers, five registered nurses, two master’s-level psychologists, and one doctoral-level psychologist. Eight of the specialists had some mental health experience, and four had some CBT training. All ACSs received 2 hr of training in issues of cultural competency, specific to patients with anxiety disorders, and a bilingual therapist delivered the CBT in Spanish at selected sites.
The ACSs used a Web-based system to enter scores for the Overall Anxiety Severity and Impairment Scale (OASIS) and a three-item version of the Patient Health Questionnaire–9 (Kroenke, Spitzer, & Williams, 2001) that were collected at each patient visit to track patient outcomes. Using the CALM model, patients who were symptomatic and thought to benefit from additional treatment with CBT or medication could receive more of the same modality (stepping up) or the alternative modality (stepping over) for up to three more steps of treatment. (For a full description of the CALM model and training, please see Craske, Roy-Byrne, et al., 2009; Rose et al., 2011; Roy-Byrne et al., 2010).
Patients in the usual care (UC) group received treatment by their physician in the usual manner (i.e., with medication, counseling or referral to a mental health specialist) with no further intervention by study clinicians. After the eligibility interview, the only contact UC patients had with study personnel was for the telephone assessments conducted by RAND.
Given the study focus on CBT treatment effects across Latinos and non-Latino Whites, only patients in the intervention condition who received CBT were included in the current analyses (i.e., those who received CBT or CBT plus medication; n = 336). Participants who were African American or identified as “other” (including Asian Americans) were not included in this study. The flowchart for screening and randomization is presented in Figure 1. A total of 1,062 of 1,620 patients (66%) who were referred were eligible for study participation. Of these, 98% (n = 1,036) consented to participate, and 1,004 were randomized. More than 80% of patients were assessed at each time point, and retention was high across both treatment groups. For a detailed review of patient flow, please see Roy-Byrne et al. (2010).
Figure 1. Recruitment flowchart for Latinos and non-Latino Whites randomized to the intervention arm of the CALM (Coordinated Anxiety Learning and Management) study. CBT = cognitive behavioral therapy.
The primary outcome measure was the 12-item Brief Symptom Inventory (BSI–12) which includes subscales of Anxiety and Somatization (Derogatis, 2001). Using procedures we have described elsewhere (Roy-Byrne et al., 2010), treatment response was operationalized as at least a 50% reduction on the BSI–12, and treatment remission was defined as a face-valid per-item score on the BSI–12 of less than 0.5 (between none and mild; total BSI–12 score < 6). Measures for secondary analyses included the Anxiety Sensitivity Index (ASI; Reiss, Peterson, Gursky, & McNally, 1986), the Patient Health Questionnaire (eight-item version, which does not include suicide item) for depression (Kroenke et al., 2001), the Sheehan Disability Scale (SDS) modified to assess anxiety-related disability (Sheehan, Harnett-Sheehan, & Raj, 1996), the Short-Form Health Survey (SF–12; i.e., Mental Health Composite Summary Scale; Ware, Kosinski, Bowker, & Gandek, 2002), and a brief survey to assess satisfaction with mental health treatment for anxiety.
These measures have been widely used in diverse populations, and both the English and Spanish versions have good psychometric properties. Specifically, the ASI has been examined in Latino clinical and nonclinical samples, and good internal consistency, test–retest reliability, and convergent validity with other anxiety measures have been reported (Cintron, Carter, Suchday, Sbrocco, & Gray, 2005; Novy, Stanley, Averill, & Daza, 2001; Sandin, Chorot, & McNally, 1996). The BSI–18 has been examined in numerous Spanish-speaking samples and demonstrates good reliability and validity; although a couple studies have revealed an inconsistent factor structure, suggesting the need for more work to establish the psychometric properties of the BSI–18 with Latinos (Galdón et al., 2008; Torres, Miller, & Moore, 2013; Wiesner et al., 2010). The Spanish version of the PHQ has been shown to have good internal consistency and concurrent and structural validity in primary care and community samples (Diez-Quevedo, Rangil, Sanchez-Planell, Kroenke, & Spitzer, 2001; Donlan & Lee, 2010; Merz, Malcarne, Roesch, Riley, & Sadler, 2011), and both the SDS and the SF–12 have been shown to be reliable and valid in Spanish-speaking primary care patients (Ayuso-Mateos, Vasquez-Barquero, Oviedo, & Diez-Manrique, 1999; Castillo, 2007; Luciano et al., 2010).
To evaluate treatment engagement, we extracted ratings from the Web-based system and computerized CBT program regarding engagement outcomes. Homework adherence, session attendance, commitment to CBT, and understanding of CBT principles were all rated by the ACS. Homework adherence was a measure of the quantity of homework completed (0 = missed none; 1 = missed few; 2 = missed half; 3 = missed most), and commitment to CBT reflected the ACS’s perception of the patient’s motivation in each CBT session (0 = none; 10 = complete). CBT understanding was based on the ACS’s perception of how well the patient understood the CBT principles being presented in each session. Patient self-report was used for outcome expectancies and self-efficacy (0 = not at all; 4 = 50/50; 8 = certainly). Outcome expectancies reflected patients’ beliefs that their participation in the CBT intervention would result in improvement, while self-efficacy reflected patients’ beliefs that they were capable of completing the requested CBT activities. These ratings were completed at all sessions, and a mean score across sessions was used for the analyses. Last, treatment completion was defined as the completion of the relapse prevention module of the CBT program (which typically occurred after eight sessions). These outcomes reflect behavioral manifestations of engagement (e.g., adherence, attendance, and drop-out) as well as aspects of treatment readiness and motivation (e.g., understanding, commitment to treatment, etc.) that influence engagement (Tetley, Jinks, Huband, & Howells, 2011).
Data Analysis
The primary aim of this study was to obtain robust estimates of the association between ethnicity (where ethnicity has only two categories: Latino and non-Latino White) and outcomes. We used propensity-score-weighted linear and logistic regression models to estimate the effect of ethnicity on clinical outcomes. Propensity-score weighting is an effective way of eliminating the differences in observed characteristics (e.g., age, gender, severity at baseline, presence of chronic medical disorders) between the Latino and non-Latino White groups that could bias the estimates of the association between ethnicity and outcomes (Rosenbaum & Rubin, 1983). In contrast, commonly used regression models rely too heavily on the linear assumption and are highly sensitive to model specification, such as the inclusion of important interaction terms.
In this application, we defined propensity score as the conditional probability that a patient is Latino, given a set of observed patient characteristics (Rosenbaum & Rubin, 1983). This probability was used to build weights (Hirano, Imbens, & Ridder, 2003; McCaffrey, Ridgeway, & Morral, 2004) for patients belonging to the non-Latino White group. Patients in the non-Latino White group who had similar characteristics to patients in the Latino group had a large propensity score, and therefore, we “up-weighted” these patients when estimating the association between ethnicity and outcomes. Patients in the non-Latino White group with characteristics dissimilar to the Latino group were “down-weighted” when we computed the effect of ethnicity. We fitted the propensity-score weights using the twang R package (Ridgeway, McCaffrey, & Morral, 2006), which uses a nonparametric regression technique instead of a logistic regression. The patients’ characteristics used to fit the propensity-score model were site; gender; age; diagnoses of PD, GAD, SAD, PTSD, or MDD; number of chronic medical conditions; income; marital status; any use of psychotropic medications prior the study start; insurance status; and baseline BSI–12 score. In this study, the obtained propensity-score weights effectively eliminated differences between the two ethnic groups for several of the characteristics used in the propensity score (PS) model, but not for all of them. In particular some differences remained for age, PTSD diagnosis, and insurance status.
In the presence of ongoing small imbalances despite PS weighting, we adopted a double robust (DR) estimation approach to further control for differences in the baseline characteristics between the two ethnic groups. DR estimation methods (Bang & Robins, 2005; Kang & Schafer, 2007) reduce the risk of bias due to remaining small differences between groups and the uncertainty in the treatment effect estimator by reducing the outcome model’s residual variance. The adopted DR estimation approach implies fitting PS-weighted linear or logistic regressions (depending on the type of outcomes) that control for, in addition to the variable indicating whether a patient is Latino or not, all the patients’ characteristics used in the PS model. This approach provided the least biased estimate of the association between ethnicity and outcomes.
Additionally, we developed three separate sets of nonresponse weights to account for missing outcome measures due to patients skipping a particular assessment (e.g., Month 6, 12, or 18) or for dropping out from the study. Nonresponse weights are an effective way to address missing data when it is due to unit nonresponse (Brick & Kalton, 1996), as was the case for the missing outcome measures. For example, missing 12-month outcomes were due to the fact that a patient failed to respond to the entire 12-month follow-up assessment, rather than a patient refusing to respond to specific questions within the assessment. The nonresponse weights were estimated in the same way as the PS weights. The aim of this method is to weigh those patients with outcomes at a given assessment (e.g., 12-month) to represent the sample of Latino and non-Latino White patients who selected CBT (n = 366).
Results Baseline Characteristics
Baseline characteristics for Latinos and non-Latino Whites are presented in Table 1. There were 85 Latino and 251 non-Latino White participants who received the CALM CBT intervention; eight Latino participants received the CBT intervention in Spanish. Patients from other ethnic groups including African Americans (n = 51) and patients who identified as “other” races/ethnicities (n = 69) were excluded from these analyses. Statistical comparisons were made using t tests for continuous variables and chi-square tests for categorical variables. Significant differences were found for age, gender, income, marital status, chronic medical conditions, PTSD, use of psychotropic medication, and insurance across ethnic groups. The Latino sample was younger, more likely to be married, and more likely to be uninsured. This sample had lower incomes and was composed disproportionately of women. Latinos also had fewer chronic medical disorders, lower rates of psychotropic medication use, and higher rates of PTSD than non-Latino Whites. As described in the statistical approach section, we controlled for all of these differences in patient characteristics using propensity weights and a DR-estimation approach. Analyses were also conducted without controlling for income and insurance, variables that often share an association with acculturation level and consequently may lead to distortions in cultural effects when controlled.
Baseline Patient Characteristics
Treatment Preference
As described earlier, participants randomized to the intervention arm were allowed to choose among the treatment options of CBT only, CBT plus medication, and medication only. Chi-square tests were used to analyze differences in treatment preference between Latinos and non-Latino Whites. Treatment preference rates did not differ significantly for Latinos and non-Latino Whites, respectively: 40% versus 36% for CBT only, 52% versus 56% for CBT plus medication, and 9% versus 8% for medication only.
Clinical Outcomes
We used PS-weighted linear and logistic regression models (DR-regression models) to estimate the effect of ethnicity on clinical outcomes. All models included baseline characteristics in addition to the Latino indicator. Only coefficients for Latino ethnicity are presented in Table 2; full models are available upon request. Significant differences were found for the survey on satisfaction with health and mental health care at 12 months and on the Mental Health Composite Scale score (MCS–12) at 18 months, with B coefficients reflecting more positive scores for Latinos at these time points. When analyses were performed without controlling for income and insurance status, findings were largely the same, except for the MCS–12 finding at 18 months, which was no longer significant (B = 2.59, p = .096; full tables are available upon request). The rates of treatment response and remission did not differ significantly between the two groups at any of the three follow-up points. Adjusted treatment response rates for Latinos ranged from 62.7% to 68.6%, while rates for non-Latino Whites ranged from 60.0% to 77.3%. Adjusted rates of remissions ranged from 41.9% to 61.5% for Latinos and from 42.8% to 62.2% for non-Latino Whites.
Double Robust Estimates of the Latino Ethnicity Effect on Clinical Outcomes
Engagement-Related Outcomes
The same analytic approach described previously was used to estimate the effects of ethnicity on engagement-related outcomes. All models controlled for baseline characteristics in addition to the Latino indicator. Only coefficients for Latino ethnicity are presented in Table 3. There were no significant differences for five of the seven engagement related variables (e.g., adherence, treatment completion, commitment to CBT, self-efficacy, outcome expectancies). Mean scores for Latinos and non-Latino Whites ranged from 8.29 to 8.52 on overall commitment to in-session CBT activities (using a 10-point scale) and from .66 to .75 for homework adherence (1 = missed few and 3 = missed most). Mean self-report ratings on treatment outcome expectancies and self-efficacy ranged from 6.3 to 6.8 on an 8-point scale. A significant difference emerged for “understanding of CBT session principles,” with Latinos receiving lower scores than non-Latino Whites. Latinos also attended fewer sessions than non-Latino Whites (adjusted mean number of sessions for Latinos was 7.44 vs. 9.09 for non-Latino Whites, p = .004). Findings remained the same, when income and insurance status were not controlled.
Double Robust Estimates of the Latino Ethnicity Effect on Engagement Outcomes
A post hoc power analysis suggested that given the sample size available, we were able to detect effect sizes in the medium range with 80% power. Effect sizes for clinical and engagement outcomes are presented in the accompanying tables.
DiscussionThe CALM study provides one of the largest samples of Latinos who have participated in an effectiveness trial for anxiety disorders and is one of the first to examine differences in CBT treatment response and engagement between Latinos and non-Latino Whites. Given the location of participating clinics (predominantly on the West Coast of the United States), a sizeable proportion of our sample identified as Hispanic/Latino (approximately 20%). Data regarding Latino ethnic subgroups and acculturation level were not gathered; however, the majority of the Latino sample was English speaking, suggesting a higher level of acculturation, and, given U.S. Census Bureau statistics from participating regions, most likely to be of Mexican origin (U.S. Census Bureau, 2011).
With regard to preference for treatment, the majority of participants from both ethnic groups chose the combination of CBT plus medication over the other treatment modalities, although a sizable number also chose CBT alone. The use of medication alone was not a common preference for either group. These findings are consistent with studies of depression that have found that both Latinos and other ethnic minorities prefer counseling approaches over medication (Cooper et al., 2003; Dwight-Johnson, Sherbourne, Liao, & Wells, 2000). Additionally, among Latinos, the use of antidepressant medication has been associated with beliefs such as greater stigma and perceptions of being more severely ill, being weak or unable to handle one’s problems, and being subjected to the negative effects of drugs (e.g., addiction; Interian, Martinez, Guarnaccia, Vega, & Escobar, 2007; Olfson, Marcus, Tedeschi, & Wan, 2006; Sirey, Bruce, Alexopoulos, Perlick, Friedman, et al., 2001; Sirey, Bruce, Alexopoulos, Perlick, Raue, et al., 2001). It is possible that Latino participants in the CALM study shared these beliefs. However, the fact that many Latino participants chose the combination approach, which included medication, may suggest greater acceptability of pharmacological approaches, particularly in the presence of a psychosocial intervention.
There were no statistically significant differences between Latinos and non-Latino Whites on measures of clinical outcome including anxiety sensitivity, depression, cognitive and somatic anxiety, and disability at any assessment point. Further, there were no significant differences between groups on indicators of treatment response or clinical remission at any time point. Significant differences did emerge for overall mental health functioning at 18 months and satisfaction with mental health care at 12 months, with Latinos having more favorable scores than non-Latino Whites. When analyses were conducted without adjusting for insurance and income, variables that are often confounded with culture, findings were largely the same. These findings parallel prior findings in child anxiety and adult depression where comparable clinical outcomes and response rates have been reported in CBT studies with Latinos and non-Latino Whites (Cardemil, Reivich, Beevers, Seligman, & James, 2007; Marchand, Ng, Rohde, & Stice, 2010; Miranda, Azócar, et al., 2003; Miranda, Duan, et al., 2003; Muñoz et al., 1995).
Based on previous studies, we expected more ethnic differences to emerge for the engagement outcomes; however, overall there were more similarities than differences. According to the ACSs, both Latino and non-Latino White participants exhibited “good” levels of homework adherence and overall commitment to session activities. Using patient self-report, both Latinos and non-Latino Whites reported favorable expectations regarding treatment outcomes and beliefs that they could complete the CBT activities. Significant differences emerged for treatment attendance and understanding of CBT principles. Latinos attended fewer sessions than non-Latino Whites, approximately seven versus nine sessions, respectively. Similarly, rates of treatment completion, defined as the completion of the relapse prevention module, tended to be higher for non-Latino Whites (75%) than Latinos (64%) although this difference did not reach statistical significance. Differences in attendance rates and premature termination have been found in other studies and have been attributed to logistic (e.g., multiple competing demands, transportation, and so on), motivational, and attitudinal factors (e.g., outcome expectancies and stigma; McCabe, 2002; Miranda, Azócar, et al., 2003; Organista et al., 1994). These explanations may also apply to participants in our study; however, as noted, patient ratings of outcome expectancies, commitment to CBT, and self-efficacy were similar for non-Latino Whites and Latinos. Ratings of satisfaction with mental health care were also similar across all time points. Further, given propensity weights for baseline differences, income-related stressors were likely not the primary cause of differential rates of attendance. Other factors, such as perceived cultural fit of the program and therapist–client ethnic match, may have had an effect on attendance but were not measured. Alternatively, it may have been that Latinos were satisfied with the number of sessions they received and did not feel the need to attend as many sessions as non-Latino Whites or to complete the intervention. The other significant difference—poorer understanding of CBT principles by Latinos—has not been reported previously in the treatment literature. It is possible that this difference may be explained by language barriers either in understanding the translation of the CBT materials or in the patients conveying their understanding of the principles to the ACSs. It may also be attributed to varying conceptualizations of anxiety disorders and their treatment, although limited data exist in this regard (Chavira et al., 2008; Hinton, 2012; Lewis-Fernández et al., 2010).
The reason for comparable clinical outcomes in the presence of differential attendance warrants some discussion. One explanation for this disconnect may be that certain aspects of engagement have a greater impact on clinical outcomes than others. For example, the impact of homework adherence on clinical outcomes has been well-established in the treatment literature (Kazantzis, Whittington, & Dattilio, 2010; Mausbach, Moore, Roesch, Cardenas, & Patterson, 2010). In the presence of good homework adherence, as noted in this study, the impact of other engagement-related variables for Latinos, such as attendance, on clinical outcomes may be mitigated. Also, previous studies support the importance of distinguishing among pretreatment, early treatment, and later stage treatment attrition (Gonzalez, Weersing, Warnick, Scahill, & Woolston, 2011; Hofmann et al., 1998; Issakidis & Andrews, 2004). Given that Latinos attended an average of seven treatment sessions, it is likely that most of the drop-out occurred at later stages, reducing the potentially more deleterious effects of early attrition on clinical outcomes. Last, the use of face-valid measures of engagement may have influenced the study findings and should be interpreted with caution. In general, efforts are necessary to further improve the definition of engagement as well as its measurement (Drieschner, Lammers, & van der Staak, 2004). Although measures of engagement exist, most are limited in scope (e.g., only address homework compliance), have limited psychometric support, and are not generalizable across populations and treatment modalities (Tetley et al., 2011). These efforts may be particularly relevant to Latinos and other underrepresented groups who are likely to encounter greater barriers to mental health services and may be more difficult to engage.
A focus on differential clinical and engagement-related outcomes between Latinos and non-Latino Whites is timely in the context of continued debate regarding cultural adaptations for evidence-based interventions (Barrera & Castro, 2006; Chu, Huynh, & Areán, 2012). According to a popular cultural adaptation framework (Lau, 2006), tailoring efforts are best guided by empirical findings that support ethnic differences in the social validity of an intervention (e.g., engagement and acceptability), clinical outcomes, or risk and resiliency factors that may affect the etiology or course of the disorder. Similar to findings in previous treatment studies for anxiety and depression (Huey & Polo, 2008; Miranda et al., 2005; Piña et al., 2003), findings from this study support mostly comparable clinical outcomes for CBT across non-Latino Whites and Latinos, specifically, English-speaking Latinos. However, findings from the current study raise some concerns regarding the social validity of the CALM intervention among Latinos given lower self-reported understanding of CBT principles, fewer sessions attended, and a trend toward lower CBT completion rates. These findings suggest that tailoring efforts to improve engagement for Latinos receiving CBT interventions like CALM may be warranted.
It is important to note that while cultural adaptations were not made to the core components or content of the CBT intervention, surface or peripheral adaptations (Resnicow, Soler, Braithwaite, Ahluwalia, & Butler, 2000; Simons-Morton, Donohew, & Davis Crump, 1997) did occur. All therapists received training in issues of cultural competency, all intervention materials were translated into Spanish including the computer program, and a bilingual therapist delivered the CBT in Spanish at selected sites; such modifications have the potential to improve the overall face validity, understanding, and acceptability of an intervention. In effect, some of the traditional barriers to access and engagement that are common among Latino populations, such as language (Morales, Cunningham, Brown, Liu, & Hays, 1999; Vega & Lopez, 2001), stigma associated with receiving mental health care at specialty care settings (Interian et al., 2007; Nadeem et al., 2007; Sirey, Bruce, Alexopoulos, Perlick, Friedman, et al., 2001), and poor therapeutic alliance due to cultural differences (Añez, Paris, Bedregal, Davidson, & Grilo, 2005; Fuertes, Boylan, & Fontanella, 2009; Vasquez, 2007) may have been addressed in the development and implementation of the CALM study.
Limitations
The CALM study was focused on the overall effectiveness of an innovative model of treatment delivery for patients with anxiety disorders in primary care; as such, it was not designed to focus on ethnic group differences, and measures of acculturation were not included in this study. While sample size allowed for the evaluation of overall main effects of ethnicity (i.e., Latino vs. non-Latino White), only effect sizes in the medium range were detectable, and thus smaller yet clinically meaningful differences may have been missed. Additionally, sample sizes were too small (n = 8) to investigate the effectiveness of the intervention for individuals who were monolingual Spanish speakers and received the intervention in Spanish. It is possible that differences in engagement may have been more substantial and differences in clinical outcomes may have emerged with a primarily Spanish-speaking sample. Understanding barriers to initial uptake and recruitment of monolingual Spanish speakers into interventions such as CALM remains an important direction of research in efficacy and effectiveness trials. The sample is also biased in that it is a primary care sample and composed of a group of individuals who chose to participate in a treatment program for anxiety. As a result, the sample may differ from community-based samples, with regard to insurance status, employment, income, access to resources, and level of acculturation. Therefore, caution is advised in generalizing these findings to lower income, Spanish-speaking, and less acculturated groups. Further, clinical outcome measures such as the Brief Symptom Inventory warrant additional psychometric examination with Latino populations from diverse acculturation levels. Last, many of our measures of engagement-related variables were face-valid items that were administered as adjunctive assessments of the therapeutic process and consequently may not have adequately examined the desired constructs.
Conclusions
Overall, findings from this study suggest that the CALM CBT program for anxiety can be an effective treatment option for Latinos who are English speaking and likely more acculturated. While current findings do not support the need for extensive tailoring of the CALM CBT intervention to meet the needs of English-speaking Latinos with anxiety disorders in primary care, findings underscore the need for continued efforts to understand and improve engagement of Latinos in evidence-based interventions. Further, additional studies with larger sample sizes, monolingual Spanish-speaking participants, and standardized measures of acculturation are warranted in order to improve the evidence base for CBT approaches with Latinos.
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Submitted: March 30, 2012 Revised: June 19, 2013 Accepted: June 21, 2013
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Source: Journal of Consulting and Clinical Psychology. Vol. 82. (3), Jun, 2014 pp. 392-403)
Accession Number: 2014-10290-001
Digital Object Identifier: 10.1037/a0036365